/Awesome-Domain-Generalization

A collection of awesome things about domain generalization, including papers, code, etc.

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Awesome Domain Generalization

This repository is a collection of awesome things about domain generalization, including papers, code, etc.

If you would like to contribute to our repository or have any questions/advice, see Contributing & Contact.

Contents

Papers

We list papers, implementation code (the unofficial code is marked with *), etc, in the order of year and from journals to conferences. Note that some papers may fall into multiple categories.

Survey

  • Domain Generalization in Vision: A Survey [arXiv 2021]
  • Generalizing to Unseen Domains: A Survey on Domain Generalization [IJCAI 2021] [Slides]

Theory & Analysis

We list the papers that either provide inspiring theoretical analyses or conduct extensive empirical studies for domain generalization.

  • A Generalization Error Bound for Multi-Class Domain Generalization [arXiv 2019]
  • Domain Generalization by Marginal Transfer Learning [JMLR 2021] [Code]
  • The Risks of Invariant Risk Minimization [ICLR 2021]
  • In Search of Lost Domain Generalization [ICLR 2021]
  • The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization [ICCV 2021] [Code]
  • An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers [NeurIPS 2021] [Code]
  • Towards a Theoretical Framework of Out-Of-Distribution Generalization [NeurIPS 2021]
  • Out-of-Distribution Generalization in Kernel Regression [NeurIPS 2021]
  • Quantifying and Improving Transferability in Domain Generalization [NeurIPS 2021] [Code]

Domain Generalization

To address the dataset/domain shift problem [108] [109] [110] [111] [112], domain generalization [113] aims to learn a model from source domain(s) and make it generalize well to unknown target domains.

Domain Alignment-Based Methods

Domain alignment-based methods aim to minimize divergence between source domains for learning domain-invariant representations.

  • Domain Generalization via Invariant Feature Representation [ICML 2013] [Code]
  • Learning Attributes Equals Multi-Source Domain Generalization [CVPR 2016]
  • Robust Domain Generalisation by Enforcing Distribution Invariance [IJCAI 2016]
  • Scatter Component Analysis A Unified Framework for Domain Adaptation and Domain Generalization [TPAMI 2017]
  • Unified Deep Supervised Domain Adaptation and Generalization [ICCV 2017] [Code]
  • Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models [arXiv 2018]
  • Domain Generalization via Conditional Invariant Representation [AAAI 2018]
  • Domain Generalization with Adversarial Feature Learning [CVPR 2018] [Code]
  • Deep Domain Generalization via Conditional Invariant Adversarial Networks [ECCV 2018]
  • Generalizing to Unseen Domains via Distribution Matching [arXiv 2019] [Code]
  • Image Alignment in Unseen Domains via Domain Deep Generalization [arXiv 2019]
  • Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection [CVPR 2019] [Code]
  • Generalizable Feature Learning in the Presence of Data Bias and Domain Class Imbalance with Application to Skin Lesion Classification [MICCAI 2019]
  • Domain Generalization via Model-Agnostic Learning of Semantic Features [NeurIPS 2019] [Code]
  • Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization [ECMLPKDD 2019] [Code]
  • Feature Alignment and Restoration for Domain Generalization and Adaptation [arXiv 2020]
  • Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations [arXiv 2020]
  • Feature alignment and restoration for domain generalization and adaptation [arXiv 2020]
  • Correlation-aware Adversarial Domain Adaptation and Generalization [PR 2020] [Code]
  • Domain Generalization Using a Mixture of Multiple Latent Domains [AAAI 2020] [Code]
  • Single-Side Domain Generalization for Face Anti-Spoofing [CVPR 2020] [Code]
  • Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRI [ISBI 2020]
  • Respecting Domain Relations: Hypothesis Invariance for Domain Generalization [ICPR 2020]
  • Domain Generalization via Multidomain Discriminant Analysis [UAI 2020] [Code]
  • Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization [NeurIPS 2020] [Code]
  • Domain Generalization via Entropy Regularization [NeurIPS 2020] [Code]
  • Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments [arXiv 2021]
  • Semi-Supervised Domain Generalization in RealWorld: New Benchmark and Strong Baseline [arXiv 2021]
  • Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization [arXiv 2021] [Code]
  • Multi-Domain Adversarial Feature Generalization for Person Re-Identification [TIP 2021]
  • Scale Invariant Domain Generalization Image Recapture Detection [ICONIP 2021]
  • Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference [IJCAI 2021]
  • Domain Generalization using Causal Matching [ICML 2021] [Code]
  • Generalization on Unseen Domains via Inference-Time Label-Preserving Target Projections [CVPR 2021] [Code]
  • Progressive Domain Expansion Network for Single Domain Generalization [CVPR 2021] [Code]
  • Confidence Calibration for Domain Generalization Under Covariate Shift [ICCV 2021]
  • On Calibration and Out-of-domain Generalization [NeurIPS 2021]

Data Augmentation-Based Methods

Data augmentation-based methods augment original data and train the model on the generated data to improve model robustness.

  • Certifying Some Distributional Robustness with Principled Adversarial Training [arXiv 2017] [Code]
  • Generalizing across Domains via Cross-Gradient Training [ICLR 2018] [Code]
  • Generalizing to Unseen Domains via Adversarial Data Augmentation [NeurIPS 2018] [Code]
  • Staining Invariant Features for Improving Generalization of Deep Convolutional Neural Networks in Computational Pathology [Frontiers in Bioengineering and Biotechnology 2019]
  • Multi-component Image Translation for Deep Domain Generalization [WACV 2019] [Code]
  • Domain Generalization by Solving Jigsaw Puzzles [CVPR 2019] [Code]
  • Addressing Model Vulnerability to Distributional Shifts Over Image Transformation Sets [ICCV 2019] [Code]
  • Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data [ICCV 2019] [Code]
  • Hallucinating Agnostic Images to Generalize Across Domains [ICCV workshop 2019]
  • Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images [Frontiers in Cardiovascular Medicine 2020]
  • Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation [TMI 2020]
  • Deep Domain-Adversarial Image Generation for Domain Generalisation [AAAI 2020] [Code]
  • Towards Universal Representation Learning for Deep Face Recognition [CVPR 2020] [Code]
  • Heterogeneous Domain Generalization via Domain Mixup [ICASSP 2020] [Code]
  • Learning to Generate Novel Domains for Domain Generalization [ECCV 2020] [Code]
  • Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization [ECCV 2020] [Code]
  • Towards Recognizing Unseen Categories in Unseen Domains [ECCV 2020] [Code]
  • Rethinking Domain Generalization Baselines [ICPR 2020]
  • More is Better: A Novel Multi-view Framework for Domain Generalization [arXiv 2021]
  • Semi-Supervised Domain Generalization with Stochastic StyleMatch [arXiv 2021] [Code]
  • Better Pseudo-label Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization [arXiv 2021]
  • Out-of-domain Generalization from a Single Source: A Uncertainty Quantification Approach [arXiv 2021]
  • Towards Principled Disentanglement for Domain Generalization [arXiv 2021] [Code]
  • MixStyle Neural Networks for Domain Generalization and Adaptation [arXiv 2021] [Code]
  • VideoDG: Generalizing Temporal Relations in Videos to Novel Domains [TPAMI 2021] [Code]
  • Domain Generalization by Marginal Transfer Learning [JMLR 2021] [Code]
  • Domain Generalisation with Domain Augmented Supervised Contrastive Learning [AAAI Student Abstract 2021]
  • DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation [AAAI 2021] [Code]
  • Domain Generalization with Mixstyle [ICLR 2021] [Code]
  • Robust and Generalizable Visual Representation Learning via Random Convolutions [ICLR 2021] [Code]
  • Learning to Learn Single Domain Generalization [CVPR 2020] [Code]
  • FSDR: Frequency Space Domain Randomization for Domain Generalization [CVPR 2021] [Code]
  • FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space [CVPR 2021] [Code]
  • Uncertainty-guided Model Generalization to Unseen Domains [CVPR 2021] [Code]
  • Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning [CVPR 2021]
  • A Fourier-Based Framework for Domain Generalization [CVPR 2021] [Code]
  • Open Domain Generalization with Domain-Augmented Meta-Learning [CVPR 2021] [Code]
  • A Simple Feature Augmentation for Domain Generalization [ICCV 2021]
  • Universal Cross-Domain Retrieval Generalizing Across Classes and Domains [ICCV 2021] [Code]
  • Feature Stylization and Domain-aware Contrastive Learning for Domain Generalization [MM 2021]
  • Adversarial Teacher-Student Representation Learning for Domain Generalization [NeurIPS 2021]
  • Model-Based Domain Generalization [NeurIPS 2021] [Code]

Meta-Learning-Based Methods

Meta-learning-based methods train the model on a meta-train set and improve its performance on a meta-test set for boosting out-of-domain generalization ability.

  • Learning to Generalize: Meta-Learning for Domain Generalization [AAAI 2018] [Code]
  • MetaReg: Towards Domain Generalization using Meta-Regularization [NeurIPS 2018] [Code*]
  • Feature-Critic Networks for Heterogeneous Domain Generalisation [ICML 2019] [Code]
  • Episodic Training for Domain Generalization [ICCV 2019] [Code]
  • Domain Generalization via Model-Agnostic Learning of Semantic Features [NeurIPS 2019] [Code]
  • Domain Generalization via Semi-supervised Meta Learning [arXiv 2020] [Code]
  • Frustratingly Simple Domain Generalization via Image Stylization [arXiv 2020] [Code]
  • Domain Generalization for Named Entity Boundary Detection via Metalearning [TNNLS 2020]
  • Learning to Learn Single Domain Generalization [CVPR 2020] [Code]
  • Learning to Learn with Variational Information Bottleneck for Domain Generalization [ECCV 2020]
  • Sequential Learning for Domain Generalization [ECCV workshop 2020]
  • Shape-Aware Meta-Learning for Generalizing Prostate MRI Segmentation to Unseen Domains [MICCAI 2020] [Code]
  • More is Better: A Novel Multi-view Framework for Domain Generalization [arXiv 2021]
  • Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains [ICIP 2021]
  • Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation [ICIP 2021]
  • MetaNorm: Learning to Normalize Few-Shot Batches Across Domains [ICLR 2021] [Code]
  • Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification [CVPR 2021] [Code]
  • Uncertainty-guided Model Generalization to Unseen Domains [CVPR 2021] [Code]
  • Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning [CVPR 2021]
  • Meta Batch-Instance Normalization for Generalizable Person Re-Identification [CVPR 2021] [Code]
  • Open Domain Generalization with Domain-Augmented Meta-Learning [CVPR 2021] [Code]
  • Exploiting Domain-Specific Features to Enhance Domain Generalization [NeurIPS 2021] [Code]

Ensemble Learning-Based Methods

Ensemble learning-based methods mainly train a domain-specific model on each source domain, and then draw on collective wisdom to make accurate prediction.

  • Exploiting Low-Rank Structure from Latent Domains for Domain Generalization [ECCV 2014]
  • Visual recognition by learning from web data: A weakly supervised domain generalization approach [CVPR 2015]
  • Multi-View Domain Generalization for Visual Recognition [ICCV 2015]
  • Deep Domain Generalization With Structured Low-Rank Constraint [TIP 2017]
  • Visual Recognition by Learning From Web Data via Weakly Supervised Domain Generalization [TNNLS 2017]
  • Robust Place Categorization with Deep Domain Generalization [IEEE Robotics and Automation Letters 2018] [Code]
  • Multi-View Domain Generalization Framework for Visual Recognition [TNNLS 2018]
  • Domain Generalization with Domain-Specific Aggregation Modules [GCPR 2018]
  • Best Sources Forward: Domain Generalization through Source-Specific Nets [ICIP 2018]
  • Batch Normalization Embeddings for Deep Domain Generalization [arXiv 2020]
  • DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets [TMI 2020]
  • MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data [TMI 2020] [Code]
  • Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition [ICLR 2020]
  • Learning to Optimize Domain Specific Normalization for Domain Generalization [ECCV 2020]
  • Class-conditioned Domain Generalization via Wasserstein Distributional Robust Optimization [ICLR workshop 2021]
  • Domain and Content Adaptive Convolution for Domain Generalization in Medical Image Segmentation [arXiv 2021]
  • Dynamically Decoding Source Domain Knowledge for Unseen Domain Generalization [arXiv 2021]
  • Domain Adaptive Ensemble Learning [TIP 2021] [Code]
  • Generalizable Person Re-identification with Relevance-aware Mixture of Experts [CVPR 2021]
  • Learning Transferrable and Interpretable Representations for Domain Generalization [MM 2021]
  • Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation [MM 2021]
  • TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification [NeurIPS 2021] [Code]

Self-Supervised Learning-Based Methods

Self-supervised learning-based methods improve model generalization by solving some pretext tasks with data itself.

  • Domain Generalization for Object Recognition with Multi-Task Autoencoders [ICCV 2015] [Code]
  • Domain Generalization by Solving Jigsaw Puzzles [CVPR 2019] [Code]
  • Improving Out-Of-Distribution Generalization via Multi-Task Self-Supervised Pretraining [arXiv 2020]
  • Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition [ICLR 2020]
  • Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization [ECCV 2020] [Code]
  • Zero Shot Domain Generalization [BMVC 2020] [Code]
  • Out-of-domain Generalization from a Single Source: A Uncertainty Quantification Approach [arXiv 2021]
  • Unsupervised Domain Generalization by Learning a Bridge Across Domains [arXiv 2021]
  • Self-Supervised Learning Across Domains [TPAMI 2021] [Code]
  • Multi-Domain Adversarial Feature Generalization for Person Re-Identification [TIP 2021]
  • Scale Invariant Domain Generalization Image Recapture Detection [ICONIP 2021]
  • Domain Generalisation with Domain Augmented Supervised Contrastive Learning [AAAI Student Abstract 2021]
  • Progressive Domain Expansion Network for Single Domain Generalization [CVPR 2021] [Code]
  • FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space [CVPR 2021] [Code]
  • Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder [ICCV 2021]
  • A Style and Semantic Memory Mechanism for Domain Generalization [ICCV 2021]
  • SelfReg: Self-Supervised Contrastive Regularization for Domain Generalization [ICCV 2021]
  • Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning [MICCAI 2021] [Code]
  • Feature Stylization and Domain-aware Contrastive Learning for Domain Generalization [MM 2021]
  • Adversarial Teacher-Student Representation Learning for Domain Generalization [NeurIPS 2021]

Disentangled Representation Learning-Based Methods

Disentangled representation learning-based methods aim to disentangle domain-specific and domain-invariant parts from source data, and then adopt the domain-invariant one for inference on the target domains.

  • Undoing the Damage of Dataset Bias [ECCV 2012] [Code]
  • Deeper, Broader and Artier Domain Generalization [ICCV 2017] [Code]
  • DIVA: Domain Invariant Variational Autoencoders [ICML workshop 2019] [Code]
  • Efficient Domain Generalization via Common-Specific Low-Rank Decomposition [ICML 2020] [Code]
  • Cross-Domain Face Presentation Attack Detection via Multi-Domain Disentangled Representation Learning [CVPR 2020]
  • Learning to Balance Specificity and Invariance for In and Out of Domain Generalization [ECCV 2020] [Code]
  • Towards Principled Disentanglement for Domain Generalization [arXiv 2021] [Code]
  • Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation [ICIP 2021]
  • DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation [AAAI 2021] [Code]
  • Robustnet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening [CVPR 2021] [Code]
  • Shape-Biased Domain Generalization via Shock Graph Embeddings [ICCV 2021]
  • Domain-Invariant Disentangled Network for Generalizable Object Detection [ICCV 2021]
  • Domain Generalization via Feature Variation Decorrelation [MM 2021]
  • Exploiting Domain-Specific Features to Enhance Domain Generalization [NeurIPS 2021] [Code]

Regularization-Based Methods

Regularization-based methods leverage regularization terms to prevent the overfitting, or design optimization strategies to guide the training.

  • Generalizing from Several Related Classification Tasks to a New Unlabeled Sample [NeurIPS 2011]
  • MetaReg: Towards Domain Generalization using Meta-Regularization [NeurIPS 2018] [Code*]
  • Invariant Risk Minimization [arXiv 2019] [Code]
  • Learning Robust Representations by Projecting Superficial Statistics Out [ICLR 2019] [Code]
  • Self-challenging Improves Cross-Domain Generalization [ECCV 2020] [Code]
  • Energy-based Out-of-distribution Detection [NeurIPS 2020] [Code]
  • Fishr: Invariant Gradient Variances for Our-of-distribution Generalization [arXiv 2021] [Code]
  • Out-of-Distribution Generalization via Risk Extrapolation [ICML 2021]
  • A Fourier-Based Framework for Domain Generalization [CVPR 2021] [Code]
  • Domain Generalization via Gradient Surgery [ICCV 2021] [Code]
  • SelfReg: Self-Supervised Contrastive Regularization for Domain Generalization [ICCV 2021]
  • Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation [MM 2021]
  • Model-Based Domain Generalization [NeurIPS 2021] [Code]
  • Swad: Domain Generalization by Seeking Flat Minima [NeurIPS 2021] [Code]
  • Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time [NeurIPS 2021] [Code]
  • Quantifying and Improving Transferability in Domain Generalization [NeurIPS 2021] [Code]

Normalization-Based Methods

Normalization-based methods calibrate data from different domains by normalizing them with their statistic.

  • Batch Normalization Embeddings for Deep Domain Generalization [arXiv 2020]
  • Learning to Optimize Domain Specific Normalization for Domain Generalization [ECCV 2020]
  • MetaNorm: Learning to Normalize Few-Shot Batches Across Domains [ICLR 2021] [Code]
  • Meta Batch-Instance Normalization for Generalizable Person Re-Identification [CVPR 2021] [Code]
  • Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification [CVPR 2021] [Code]
  • Adversarially Adaptive Normalization for Single Domain Generalization [CVPR 2021]
  • Collaborative Optimization and Aggregation for Decentralized Domain Generalization and Adaptation [ICCV 2021]
  • Domain Generalization through Audio-Visual Relative Norm Alignment in First Person Action Recognition [WACV 2022]

Information-Based Methods

Information-based methods utilize techniques of information theory to realize domain generalization.

  • Learning to Learn with Variational Information Bottleneck for Domain Generalization [ECCV 2020]
  • Progressive Domain Expansion Network for Single Domain Generalization [CVPR 2021] [Code]
  • Learning To Diversify for Single Domain Generalization [ICCV 2021] [Code]
  • Invariance Principle Meets Information Bottleneck for Out-Of-Distribution Generalization [NeurIPS 2021] [Code]
  • Exploiting Domain-Specific Features to Enhance Domain Generalization [NeurIPS 2021] [Code]
  • Invariant Information Bottleneck for Domain Generalization [AAAI 2022] [Code]

Causality-Based Methods

Causality-based methods analyze and address the domain generalization problem from a causal perspective.

  • Invariant Risk Minimization [arXiv 2019] [Code]
  • Learning Domain-Invariant Relationship with Instrumental Variable for Domain Generalization [arXiv 2021]
  • A Causal Framework for Distribution Generalization [TPAMI 2021] [Code]
  • Domain Generalization using Causal Matching [ICML 2021] [Code]
  • Deep Stable Learning for Out-of-Distribution Generalization [CVPR 2021] [Code]
  • A Style and Semantic Memory Mechanism for Domain Generalization [ICCV 2021]
  • Learning Causal Semantic Representation for Out-of-Distribution Prediction [NeurIPS 2021] [Code]
  • Recovering Latent Causal Factor for Generalization to Distributional Shifts [NeurIPS 2021] [Code]
  • On Calibration and Out-of-domain Generalization [NeurIPS 2021]
  • Invariance Principle Meets Information Bottleneck for Out-Of-Distribution Generalization [NeurIPS 2021] [Code]
  • Invariant Information Bottleneck for Domain Generalization [AAAI 2022] [Code]

Inference-Time-Based Methods

Inference-time-based methods leverage the unlabeled target data, which is available at inference-time, to improve generalization performance without further model training.

  • Generalization on Unseen Domains via Inference-Time Label-Preserving Target Projections [CVPR 2021] [Code]
  • Adaptive Methods for Real-World Domain Generalization [CVPR 2021] [Code]
  • Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization [NeurIPS 2021] [Code]

Neural Architecture Search-based Methods

Neural architecture search-based methods aim to dynamically tune the network architecture to improve out-of-domain generalization.

  • NAS-OoD Neural Architecture Search for Out-of-Distribution Generalization [ICCV 2021]

Single Domain Generalization

The goal of single domain generalization task is to improve model performance on unknown target domains by using data from only one source domain.

  • Learning to Learn Single Domain Generalization [CVPR 2020] [Code]
  • Out-of-domain Generalization from a Single Source: A Uncertainty Quantification Approach [arXiv 2021]
  • Uncertainty-guided Model Generalization to Unseen Domains [CVPR 2021] [Code]
  • Adversarially Adaptive Normalization for Single Domain Generalization [CVPR 2021]
  • Progressive Domain Expansion Network for Single Domain Generalization [CVPR 2021] [Code]
  • Learning To Diversify for Single Domain Generalization [ICCV 2021] [Code]

Semi/Weak/Un-Supervised Domain Generalization

Semi/weak-supervised domain generalization assumes that a part of the source data is unlabeled, while unsupervised domain generalization assumes no training supervision.

  • Visual recognition by learning from web data: A weakly supervised domain generalization approach [CVPR 2015]
  • Visual Recognition by Learning From Web Data via Weakly Supervised Domain Generalization [TNNLS 2017]
  • Domain Generalization via Semi-supervised Meta Learning [arXiv 2020] [Code]
  • Deep Semi-supervised Domain Generalization Network for Rotary Machinery Fault Diagnosis under Variable Speed [IEEE Transactions on Instrumentation and Measurement 2020]
  • Semi-Supervised Domain Generalization with Stochastic StyleMatch [arXiv 2021] [Code]
  • Better Pseudo-label Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization [arXiv 2021]
  • Semi-Supervised Domain Generalization in RealWorld: New Benchmark and Strong Baseline [arXiv 2021]
  • Unsupervised Domain Generalization by Learning a Bridge Across Domains [arXiv 2021]
  • Domain-Specific Bias Filtering for Single Labeled Domain Generalization [arXiv 2021] [Code]

Open/Heterogeneous Domain Generalization

Open/heterogeneous domain generalization assumes the label space of one domain is different from that of another domain.

  • Feature-Critic Networks for Heterogeneous Domain Generalisation [ICML 2019] [Code]
  • Episodic Training for Domain Generalization [ICCV 2019] [Code]
  • Towards Recognizing Unseen Categories in Unseen Domains [ECCV 2020] [Code]
  • Heterogeneous Domain Generalization via Domain Mixup [ICASSP 2020] [Code]
  • Open Domain Generalization with Domain-Augmented Meta-Learning [CVPR 2021] [Code]
  • Universal Cross-Domain Retrieval Generalizing Across Classes and Domains [ICCV 2021] [Code]

Federated Domain Generalization

Federated domain generalization assumes that source data is distributed and can not be fused for data privacy protection.

  • Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization [arXiv 2021] [Code]
  • FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space [CVPR 2021] [Code]
  • Collaborative Optimization and Aggregation for Decentralized Domain Generalization and Adaptation [ICCV 2021]

Applications

Person Re-Identification

  • Deep Domain-Adversarial Image Generation for Domain Generalisation [AAAI 2020] [Code]
  • Learning to Generate Novel Domains for Domain Generalization [ECCV 2020] [Code]
  • Learning Generalisable Omni-Scale Representations for Person Re-Identification [TPAMI 2021] [Code]
  • Multi-Domain Adversarial Feature Generalization for Person Re-Identification [TIP 2021]
  • Domain Generalization with Mixstyle [ICLR 2021] [Code]
  • Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification [CVPR 2021] [Code]
  • Meta Batch-Instance Normalization for Generalizable Person Re-Identification [CVPR 2021] [Code]
  • Generalizable Person Re-identification with Relevance-aware Mixture of Experts [CVPR 2021]
  • TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification [NeurIPS 2021] [Code]

Face Recognition & Anti-Spoofing

  • Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection [CVPR 2019] [Code]
  • Towards Universal Representation Learning for Deep Face Recognition [CVPR 2020] [Code]
  • Cross-Domain Face Presentation Attack Detection via Multi-Domain Disentangled Representation Learning [CVPR 2020]
  • Single-Side Domain Generalization for Face Anti-Spoofing [CVPR 2020] [Code]

Related Topics

Life-Long Learning

  • Sequential Learning for Domain Generalization [ECCV workshop 2020]
  • Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning [CVPR 2021]

Datasets

Evaluations on the following datasets often follow leave-one-domain-out protocol: randomly choose one domain to hold out as the target domain, while the others are used as the source domain(s).

Datasets (download link) Description Related papers in Paper Index
Colored MNIST [165] Handwritten digit recognition; 3 domains: {0.1, 0.3, 0.9}; 70,000 samples of dimension (2, 28, 28); 2 classes [82], [138], [140], [149], [152], [154], [165], [171], [173], [190], [200], [202]
Rotated MNIST [6] (original) Handwritten digit recognition; 6 domains with rotated degree: {0, 15, 30, 45, 60, 75}; 7,000 samples of dimension (1, 28, 28); 10 classes [5], [6], [15], [35], [53], [55], [63], [71], [73], [74], [76], [77], [86], [90], [105], [107], [138], [140], [170], [173], [202], [204], [206]
Digits-DG [28] Handwritten digit recognition; 4 domains: {MNIST [29], MNIST-M [30], SVHN [31], SYN [30]}; 24,000 samples; 10 classes [21], [25], [27], [28], [34], [35], [55], [59], [63], [69], [94], [98], [116], [118], [130], [141], [142], [146], [151], [153], [157], [158], [159], [160], [166], [168], [179], [189], [203]
VLCS [16] (1; or original) Object recognition; 4 domains: {Caltech [8], LabelMe [9], PASCAL [10], SUN [11]}; 10,729 samples of dimension (3, 224, 224); 5 classes; about 3.6 GB [2], [6], [7], [14], [15], [18], [60], [61], [64], [67], [68], [70], [71], [74], [76], [77], [81], [83], [86], [91], [98], [99], [101], [102], [103], [117], [118], [126], [127], [131], [132], [136], [138], [140], [142], [145], [146], [148], [149], [161], [170], [173], [174], [184], [190], [195], [199], [201], [202], [203]
Office31+Caltech [32] (1) Object recognition; 4 domains: {Amazon, Webcam, DSLR, Caltech}; 4,652 samples in 31 classes (office31) or 2,533 samples in 10 classes (office31+caltech); 51 MB [6], [35], [67], [68], [70], [71], [80], [91], [96], [119], [131], [167]
OfficeHome [20] (1; or original) Object recognition; 4 domains: {Art, Clipart, Product, Real World}; 15,588 samples of dimension (3, 224, 224); 65 classes; 1.1 GB [19], [54], [28], [34], [55], [58], [60], [61], [64], [69], [80], [92], [94], [98], [101], [118], [126], [130], [131], [132], [133], [137], [138], [140], [146], [148], [156], [159], [160], [162], [163], [167], [173], [174], [178], [179], [184], [189], [190], [199], [201], [202], [203], [206]
PACS [2] (1; or original) Object recognition; 4 domains: {photo, art_painting, cartoon, sketch}; 9,991 samples of dimension (3, 224, 224); 7 classes; 174 MB [1], [2], [4], [5], [14], [15], [18], [19], [34], [54], [28], [35], [55], [56], [57], [58], [59], [60], [61], [64], [69], [73], [77], [80], [81], [82], [83], [84], [86], [90], [92], [94], [96], [98], [99], [101], [102], [104], [105], [116], [117], [118], [127], [129], [130], [131], [132], [136], [137], [138], [139], [140], [142], [145], [146], [148], [149], [153], [156], [157], [158], [159], [160], [161], [162], [163], [167], [170], [171], [173], [174], [178], [179], [180], [184], [189], [190], [195], [199], [200], [201], [202], [203], [206]
DomainNet [33] (clipart, infograph, painting, quick-draw, real, and sketch; or original) Object recognition; 6 domains: {clipart, infograph, painting, quick-draw, real, sketch}; 586,575 samples of dimension (3, 224, 224); 345 classes; 1.2 GB + 4.0 GB + 3.4 GB + 439 MB + 5.6 GB + 2.5 GB [34], [57], [104], [119], [130], [131], [132], [133], [138], [140], [150], [173], [178], [189], [201], [202], [203]
mini-DomainNet [34] Object recognition; a smaller and less noisy version of DomainNet; 4 domains: {clipart, painting, real, sketch}; 140,006 samples [34], [69], [130], [156], [157]
ImageNet-Sketch [35] Object recognition; 2 domains: {real, sketch}; 50,000 samples [64]
VisDA-17 [36] Object recognition; 3 domains of synthetic-to-real generalization; 280,157 samples [119], [178]
CIFAR-10-C / CIFAR-100-C / ImageNet-C [37] (original) Object recognition; the test data are damaged by 15 corruptions (each with 5 intensity levels) drawn from 4 categories (noise, blur, weather, and digital); 60,000/60,000/1.3M samples [27], [74], [116], [141], [151], [168]
Visual Decathlon (VD) [38] Object/action/handwritten/digit recognition; 10 domains from the combination of 10 datasets; 1,659,142 samples [5], [7], [128]
IXMAS [39] Action recognition; 5 domains with 5 camera views, 10 subjects, and 5 actions; 1,650 samples [7], [14], [67], [76]
SYNTHIA [42] Semantic segmentation; 15 domains with 4 locations and 5 weather conditions; 2,700 samples [27], [62], [115], [141], [151], [185], [193]
GTA5-Cityscapes [43], [44] Semantic segmentation; 2 domains of synthetic-to-real generalization; 29,966 samples [62], [115], [185], [193]
Terra Incognita (TerraInc) [45] (1 and 2; or original) Animal classification; 4 domains captured at different geographical locations: {L100, L38, L43, L46}; 24,788 samples of dimension (3, 224, 224); 10 classes; 6.0 GB + 8.6 MB [132], [136], [138], [140], [173], [201], [202], [207]
Market-Duke [46], [47] Person re-idetification; cross-dataset re-ID; heterogeneous DG with 2 domains; 69,079 samples [12], [13], [28], [55], [56], [58], [114], [144], [187], [208]

Libraries

We list the GitHub libraries of domain generalization (sorted by stars).

Lectures & Tutorials & Talks

  • (Talk 2021) Generalizing to Unseen Domains: A Survey on Domain Generalization [155]. [Video] [Slides] (Jindong Wang (MSRA), in Chinese)

Other Resources

  • A collection of domain generalization papers organized by amber0309.
  • A collection of domain generalization papers organized by jindongwang.
  • A collection of papers on domain generalization, domain adaptation, causality, robustness, prompt, optimization, generative model, etc, organized by yfzhang114.
  • Adaptation and Generalization Across Domains in Visual Recognition with Deep Neural Networks [PhD 2020, Kaiyang Zhou (University of Surrey)]

Paper Index

We list all the papers for quick check, including method abbreviation, keywords, etc.

[1] Learning to Generalize: Meta-Learning for Domain Generalization [AAAI 2018] [Code] (MLDG, meta-learning)

[2] Deeper, Broader and Artier Domain Generalization [ICCV 2017] [Code] (disentangled representation learning, PACS dataset)

[3] Domain Generalization in Vision: A Survey [arXiv 2021] (survey)

[4] MetaReg: Towards Domain Generalization using Meta-Regularization [NeurIPS 2018] [Code*] (MetaReg, meta-learning, regularization)

[5] Feature-Critic Networks for Heterogeneous Domain Generalisation [ICML 2019] [Code] (Feature-Critic, meta-learning, open/heterogeneous domain generalization)

[6] Domain Generalization for Object Recognition with Multi-Task Autoencoders [ICCV 2015] [Code] (MTAE, self-supervised learning, Rotated MNIST dataset)

[7] Episodic Training for Domain Generalization [ICCV 2019] [Code] (Epi-FCR, meta-learning, open/heterogeneous domain generalization)

[8] Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories [CVPR workshop 2004] (Caltech dataset)

[9] Labelme: A Database and Web-Based Tool for Image Annotation [IJCV 2008] (LabelMe dataset)

[10] The pascal visual object classes (voc) challenge [IJCV 2010] (PASCAL dataset)

[11] Sun Database: Large-Scale Scene Recognition from Abbey to Zoo [CVPR 2010] (Sun dataset)

[12] Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification [CVPR 2021] [Code] (M3L, meta-learning, normalization, person re-identification)

[13] Meta Batch-Instance Normalization for Generalizable Person Re-Identification [CVPR 2021] [Code] (MetaBIN, meta-learning, normalization, person re-identification)

[14] Sequential Learning for Domain Generalization [ECCV workshop 2020] (S-MLDG, meta-learning, life-long learning)

[15] Learning to Learn with Variational Information Bottleneck for Domain Generalization [ECCV 2020] (MetaVIB, meta-learning, information)

[16] Unbiased Metric Learning: On the Utilization of Multiple Datasets and Web Images for Softening Bias [ICCV 2013] (VLCS dataset)

[17] Shape-Aware Meta-Learning for Generalizing Prostate MRI Segmentation to Unseen Domains [MICCAI 2020] [Code] (SAML, meta-learning)

[18] Domain Generalization via Model-Agnostic Learning of Semantic Features [NeurIPS 2019] [Code] (MASF, domain alignment, meta-learning)

[19] MetaNorm: Learning to Normalize Few-Shot Batches Across Domains [ICLR 2021] [Code] (MetaNorm, meta-learning, normalization)

[20] Deep Hashing Network for Unsupervised Domain Adaptation [CVPR 2017] [Code] (OfficeHome dataset)

[21] Addressing Model Vulnerability to Distributional Shifts Over Image Transformation Sets [ICCV 2019] [Code] (data augmentation)

[22] Towards Universal Representation Learning for Deep Face Recognition [CVPR 2020] [Code] (data augmentation, face recognition & anti-spoofing)

[23] Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation [TMI 2020] (BigAug, data augmentation)

[24] Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images [Frontiers in Cardiovascular Medicine 2020] (data augmentation)

[25] Generalizing to Unseen Domains via Adversarial Data Augmentation [NeurIPS 2018] [Code] (data augmentation)

[26] Staining Invariant Features for Improving Generalization of Deep Convolutional Neural Networks in Computational Pathology [Frontiers in Bioengineering and Biotechnology 2019] (data augmentation)

[27] Learning to Learn Single Domain Generalization [CVPR 2020] [Code] (M-ADA, data augmentation, meta-learning, single domain generalization)

[28] Learning to Generate Novel Domains for Domain Generalization [ECCV 2020] [Code] (L2A-OT, data augmentation, person re-identification, Digits-DG dataset)

[29] Gradient-Based Learning Applied to Document Recognition [IEEE 1998] (MNIST dataset)

[30] Unsupervised Domain Adaptation by Backpropagation [ICML 2015] (MNIST-M dataset)

[31] Reading Digits in Natural Images with Unsupervised Feature Learning [NeurIPS workshop 2011] (SVHN dataset)

[32] Adapting Visual Category Models to New Domains [ECCV 2010] (Office31 dataset)

[33] Moment Matching for Multi-Source Domain Adaptation [ICCV 2019] (DomainNet dataset)

[34] Domain Adaptive Ensemble Learning [TIP 2021] [Code] (ensemble learning, mini-DomainNet dataset)

[35] Learning Robust Representations by Projecting Superficial Statistics Out [ICLR 2019] [Code] (HEX, ImageNet-Sketch dataset, regularization)

[36] Visda: The visual domain adaptation challenge [arXiv 2017] (Visda dataset)

[37] Benchmarking Neural Network Robustness to Common Corruptions and Perturbations [ICLR 2019] (CIFAR-10-C, CIFAR-100-C, ImageNet-C datasets)

[38] Learning Multiple Visual Domains with Residual Adapters [NeurIPS 2017] (Visual Decathlon dataset)

[39] Free Viewpoint Action Recognition Using Mmotion History Volumes [CVIU 2006] (IXMAS dataset)

[40] Ucf101: A dataset of 101 Human Actions Classes from Videos in the Wild [arXiv 2012] (UCF-HMDB dataset)

[41] Hmdb: Large Video Database for Human Motion Recognition [ICCV 2011] (UCF-HMDB dataset)

[42] The Synthia Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes [CVPR 2016] (SYNTHIA dataset)

[43] Playing for Data: Ground Truth from Computer Games [ECCV 2016] (GTA5-Cityscapes dataset)

[44] The Cityscapes Dataset for Semantic Urban Scene Understanding [CVPR 2016] (GTA5-Cityscapes dataset)

[45] Recognition in Terra Incognita [ECCV 2018] (TerraInc dataset)

[46] Scalable Person Re-Identification: A Benchmark [ICCV 2015] (Market-Duke dataset)

[47] Performance Measures and a Data Set for Multi-target, Multi-Camera Tracking [ECCV 2016] (Market-Duke dataset)

[48] A Face Antispoofing Database with Diverse Attacks [ICB 2012] (COMI dataset)

[49] Oulu-npu: A Mobile Face Presentation Attack Database with Realworld Variations [FG 2017] (COMI dataset)

[50] Face Spoof Detection with Image Distortion Analysis [TIFS 2015] (COMI dataset)

[51] On the Effectiveness of Local Binary Patterns in Face Anti-Spoofing [BIOSIG 2012] (COMI dataset)

[52] Certifying Some Distributional Robustness with Principled Adversarial Training [arXiv 2017] [Code] (data augmentation)

[53] Generalizing across Domains via Cross-Gradient Training [ICLR 2018] [Code] (CrossGrad, data augmentation)

[54] Semi-Supervised Domain Generalization with Stochastic StyleMatch [arXiv 2021] [Code] (StyleMatch, data augmentation, semi/weak/un-supervised domain generalization)

[55] Deep Domain-Adversarial Image Generation for Domain Generalisation [AAAI 2020] [Code] (DDAIG, data augmentation, person re-identification)

[56] Domain Generalization with Mixstyle [ICLR 2021] [Code] (MixStyle, data augmentation, person re-identification)

[57] Towards Recognizing Unseen Categories in Unseen Domains [ECCV 2020] [Code] (CuMix, data augmentation, open/heterogeneous domain generalization)

[58] MixStyle Neural Networks for Domain Generalization and Adaptation [arXiv 2021] [Code] (MixStyle, data augmentation)

[59] Robust and Generalizable Visual Representation Learning via Random Convolutions [ICLR 2021] [Code] (RC, data augmentation)

[60] Frustratingly Simple Domain Generalization via Image Stylization [arXiv 2020] [Code] (data augmentation)

[61] Rethinking Domain Generalization Baselines [ICPR 2020] (data augmentation)

[62] Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data [ICCV 2019] [Code] (data augmentation)

[63] Hallucinating Agnostic Images to Generalize Across Domains [ICCV workshop 2019] [Code] (data augmentation)

[64] Self-challenging Improves Cross-Domain Generalization [ECCV 2020] [Code] (RSC, regularization)

[65] Domain Generalization via Invariant Feature Representation [ICML 2013] [Code] (DICA, domain alignment)

[66] Robust Domain Generalisation by Enforcing Distribution Invariance [IJCAI 2016] (ESRand, domain alignment)

[67] Scatter Component Analysis A Unified Framework for Domain Adaptation and Domain Generalization [TPAMI 2017] (SCA, domain alignment)

[68] Domain Generalization via Conditional Invariant Representation [AAAI 2018] (CIDG, domain alignment)

[69] Feature alignment and restoration for domain generalization and adaptation [arXiv 2020] (FAR, domain alignment)

[70] Domain Generalization via Multidomain Discriminant Analysis [UAI 2020] [Code] (MDA, domain alignment)

[71] Unified Deep Supervised Domain Adaptation and Generalization [ICCV 2017] [Code] (CCSA, domain alignment)

[72] Generalizable Feature Learning in the Presence of Data Bias and Domain Class Imbalance with Application to Skin Lesion Classification [MICCAI 2019] (domain alignment)

[73] Domain Generalization using Causal Matching [ICML 2021] [Code] (MatchDG, domain alignment, causality)

[74] Respecting Domain Relations: Hypothesis Invariance for Domain Generalization [ICPR 2020] (HIR, domain alignment)

[75] Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization [NeurIPS 2020] [Code] (LDDG, domain alignment)

[76] Domain Generalization with Adversarial Feature Learning [CVPR 2018] [Code] (MMD-AAE, domain alignment)

[77] Deep Domain Generalization via Conditional Invariant Adversarial Networks [ECCV 2018] (CIDDG, domain alignment)

[78] Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection [CVPR 2019] [Code] (MADDG, domain alignment, face recognition & anti-spoofing)

[79] Single-Side Domain Generalization for Face Anti-Spoofing [CVPR 2020] [Code] (SSDG, domain alignment, face recognition & anti-spoofing)

[80] Correlation-aware Adversarial Domain Adaptation and Generalization [PR 2020] [Code] (CAADA, domain alignment)

[81] Generalizing to Unseen Domains via Distribution Matching [arXiv 2019] [Code] (G2DM, domain alignment)

[82] Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations [arXiv 2020] (RVR, domain alignment)

[83] Domain Generalization Using a Mixture of Multiple Latent Domains [AAAI 2020] [Code] (domain alignment)

[84] Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization [ECMLPKDD 2019] [Code] (AFLAC, domain alignment)

[85] Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRI [ISBI 2020] (domain alignment)

[86] Domain Generalization via Entropy Regularization [NeurIPS 2020] [Code] (domain alignment)

[87] Exploiting Low-Rank Structure from Latent Domains for Domain Generalization [ECCV 2014] (ensemble learning)

[88] Multi-View Domain Generalization for Visual Recognition [ICCV 2015] (MVDG, ensemble learning)

[89] Visual recognition by learning from web data: A weakly supervised domain generalization approach [CVPR 2015] (ensemble learning, semi/weak/un-supervised domain generalization)

[90] Best Sources Forward: Domain Generalization through Source-Specific Nets [ICIP 2018] (ensemble learning)

[91] Deep Domain Generalization With Structured Low-Rank Constraint [TIP 2017] (ensemble learning)

[92] Domain Generalization with Domain-Specific Aggregation Modules [GCPR 2018] (D-SAMs, ensemble learning)

[93] DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets [TMI 2020] (DoFE, ensemble learning)

[94] Learning to Optimize Domain Specific Normalization for Domain Generalization [ECCV 2020] (DSON, ensemble learning, normalization)

[95] MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data [TMI 2020] [Code] (MS-Net, ensemble learning)

[96] Batch Normalization Embeddings for Deep Domain Generalization [arXiv 2020] (BNE, ensemble learning, normalization)

[97] Robust Place Categorization with Deep Domain Generalization [IEEE Robotics and Automation Letters 2018] [Code] (COLD, ensemble learning)

[98] Domain Generalization by Solving Jigsaw Puzzles [CVPR 2019] [Code] (JiGen, data augmentation, self-supervised learning)

[99] Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization [ECCV 2020] [Code] (EISNet, data augmentation, self-supervised learning)

[100] Zero Shot Domain Generalization [BMVC 2020] [Code] (self-supervised learning)

[101] Self-Supervised Learning Across Domains [TPAMI 2021] [Code] (self-supervised learning)

[102] Improving Out-Of-Distribution Generalization via Multi-Task Self-Supervised Pretraining [arXiv 2020] (self-supervised learning)

[103] Undoing the Damage of Dataset Bias [ECCV 2012] [Code] (disentangled representation learning)

[104] Learning to Balance Specificity and Invariance for In and Out of Domain Generalization [ECCV 2020] [Code] (DMG, disentangled representation learning)

[105] Efficient Domain Generalization via Common-Specific Low-Rank Decomposition [ICML 2020] [Code] (CSD, disentangled representation learning)

[106] Cross-Domain Face Presentation Attack Detection via Multi-Domain Disentangled Representation Learning [CVPR 2020] (disentangled representation learning, face recognition/antispoofing)

[107] DIVA: Domain Invariant Variational Autoencoders [ICML workshop 2019] [Code] (DIVA, disentangled representation learning)

[108] Dataset Shift in Machine Learning [MIT 2019] (dataset shift)

[109] A Unifying View on Dataset Shift in Classification [PR 2012] (dataset shift)

[110] Do Imagenet Classifiers Generalize to Imagenet? [ICML 2019] (dataset shift)

[111] A Theory of Learning from Different Domains [ML 2010] (dataset shift)

[112] Measuring Robustness to Natural Distribution Shifts in Image Classification [NeurIPS 2020] [Code] (dataset shift)

[113] Generalizing from Several Related Classification Tasks to a New Unlabeled Sample [NeurIPS 2011] (domain generalization)

[114] Learning Generalisable Omni-Scale Representations for Person Re-Identification [TPAMI 2021] [Code] (person re-identification)

[115] FSDR: Frequency Space Domain Randomization for Domain Generalization [CVPR 2021] [Code] (FSDR, data augmentation)

[116] Adversarially Adaptive Normalization for Single Domain Generalization [CVPR 2021] (ASR, normalization, single domain generalization)

[117] Deep Stable Learning for Out-of-Distribution Generalization [CVPR 2021] [Code] (StableNet, causality)

[118] Generalization on Unseen Domains via Inference-Time Label-Preserving Target Projections [CVPR 2021] [Code] (domain alignment, inference-time)

[119] Open Domain Generalization with Domain-Augmented Meta-Learning [CVPR 2021] [Code] (DAML, data augmentation, meta-learning, open/heterogeneous domain generalization)

[120] Learning Attributes Equals Multi-Source Domain Generalization [CVPR 2016] (UDICA, domain alignment)

[121] Visual Recognition by Learning From Web Data via Weakly Supervised Domain Generalization [TNNLS 2017] (ensemble learning, semi/weak/un-supervised domain generalization)

[122] Multi-View Domain Generalization Framework for Visual Recognition [TNNLS 2018] (ensemble learning)

[123] A Generalization Error Bound for Multi-Class Domain Generalization [arXiv 2019] (theory & analysis)

[124] Domain Generalization for Named Entity Boundary Detection via Metalearning [TNNLS 2020] (METABDRY, meta-learning)

[125] Deep Semi-supervised Domain Generalization Network for Rotary Machinery Fault Diagnosis under Variable Speed [IEEE Transactions on Instrumentation and Measurement 2020] (DSDGN, semi/weak/un-supervised domain generalization)

[126] Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition [ICLR 2020] (GCFN, ensemble learning, self-supervised learning)

[127] Domain Generalization via Semi-supervised Meta Learning [arXiv 2020] [Code] (DGSML, meta-learning, semi/weak/un-supervised domain generalization)

[128] Heterogeneous Domain Generalization via Domain Mixup [ICASSP 2020] [Code] (data augmentation, open/heterogeneous domain generalization)

[129] NAS-OoD Neural Architecture Search for Out-of-Distribution Generalization [ICCV 2021] (NAS-OoD, neural architecture search)

[130] A Style and Semantic Memory Mechanism for Domain Generalization [ICCV 2021] (STEAM, self-supervised learning, causality)

[131] Learning Transferrable and Interpretable Representations for Domain Generalization [MM 2021] (DTN, ensemble learning)

[132] Adaptive Methods for Real-World Domain Generalization [CVPR 2021] [Code] (DA-ERM, inference-time)

[133] Confidence Calibration for Domain Generalization Under Covariate Shift [ICCV 2021] (domain alignment)

[134] In Search of Lost Domain Generalization [ICLR 2021] (theory & analysis)

[135] The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization [ICCV 2021] [Code] (theory & analysis)

[136] Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization [NeurIPS 2021] [Code] (T3A, inference-time)

[137] Feature Stylization and Domain-aware Contrastive Learning for Domain Generalization [MM 2021] (data augmentation, self-supervised learning)

[138] SelfReg: Self-Supervised Contrastive Regularization for Domain Generalization [ICCV 2021] (SelfReg, self-supervised learning, regularization)

[139] Domain Generalisation with Domain Augmented Supervised Contrastive Learning [AAAI Student Abstract 2021] (DASCL, data augmentation, self-supervised learning)

[140] Invariant Information Bottleneck for Domain Generalization [AAAI 2022] [Code] (IIB, information, causality)

[141] Progressive Domain Expansion Network for Single Domain Generalization [CVPR 2021] [Code] (PDEN, domain alignment, self-supervised learning, information, single domain generalization)

[142] A Simple Feature Augmentation for Domain Generalization [ICCV 2021] (SFA, data augmentation)

[143] Domain-Invariant Disentangled Network for Generalizable Object Detection [ICCV 2021] (disentangled representation learning)

[144] Multi-Domain Adversarial Feature Generalization for Person Re-Identification [TIP 2021] (MMFA-AAE, domain alignment, self-supervised learning, person re-identification)

[145] Learning Causal Semantic Representation for Out-of-Distribution Prediction [NeurIPS 2021] [Code] (CSG-ind, causality)

[146] Domain Generalization via Feature Variation Decorrelation [MM 2021] (disentangled representation learning)

[147] FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space [CVPR 2021] [Code] (FedDG, data augmentation, self-supervised learning, federated domain generalization)

[148] Domain Generalization via Gradient Surgery [ICCV 2021] [Code] (Agr, regularization)

[149] Shape-Biased Domain Generalization via Shock Graph Embeddings [ICCV 2021] (disentangled representation learning)

[150] Universal Cross-Domain Retrieval Generalizing Across Classes and Domains [ICCV 2021] [Code] (SnMpNet, data augmentation, open/heterogeneous domain generalization)

[151] Out-of-domain Generalization from a Single Source: A Uncertainty Quantification Approach [arXiv 2021] (data augmentation, self-supervised learning, single domain generalization)

[152] Recovering Latent Causal Factor for Generalization to Distributional Shifts [NeurIPS 2021] [Code] (LaCIM, causality)

[153] Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning [CVPR 2021] (Meta-DR, data augmentation, meta-learning, life-long learning)

[154] On Calibration and Out-of-domain Generalization [NeurIPS 2021] (domain alignment, causality)

[155] Generalizing to Unseen Domains: A Survey on Domain Generalization [IJCAI 2021] [Slides] (survey)

[156] Better Pseudo-label Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization [arXiv 2021] (data augmentation, semi/weak/un-supervised domain generalization)

[157] Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation [MM 2021] (KDDG, ensemble learning, regularization)

[158] Learning To Diversify for Single Domain Generalization [ICCV 2021] [Code] (information, single domain generalization)

[159] Collaborative Optimization and Aggregation for Decentralized Domain Generalization and Adaptation [ICCV 2021] (COPDA, normalization, federated domain generalization)

[160] A Fourier-Based Framework for Domain Generalization [CVPR 2021] [Code] (FACT, data augmentation, regularization)

[161] Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization [arXiv 2021] [Code] (CSAC, domain alignment, federated domain generalization)

[162] Domain-Specific Bias Filtering for Single Labeled Domain Generalization [arXiv 2021] [Code] (DSBF, semi/weak/un-supervised domain generalization)

[163] Learning Domain-Invariant Relationship with Instrumental Variable for Domain Generalization [arXiv 2021] (DRIVE, causality)

[164] Adaptation and Generalization Across Domains in Visual Recognition with Deep Neural Networks [PhD 2020] (other resources)

[165] Invariant Risk Minimization [arXiv 2019] [Code] (IRM, regularization, causality)

[166] Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models [arXiv 2018] (UNVP, domain alignment)

[167] Multi-component Image Translation for Deep Domain Generalization [WACV 2019] [Code] (data augmentation)

[168] Uncertainty-guided Model Generalization to Unseen Domains [CVPR 2021] [Code] (data augmentation, meta-learning, single domain generalization)

[169] Image Alignment in Unseen Domains via Domain Deep Generalization [arXiv 2019] (DeGIA, domain alignment)

[170] Towards Principled Disentanglement for Domain Generalization [arXiv 2021] [Code] (DDG, data augmentation, disentangled representation learning)

[171] DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation [AAAI 2021] [Code] (DecAug, data augmentation, disentangled representation learning)

[172] Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning [MICCAI 2021] [Code] (MSVCL, self-supervised learning)

[173] Fishr: Invariant Gradient Variances for Our-of-distribution Generalization [arXiv 2021] [Code] (Fishr, regularization)

[174] Dynamically Decoding Source Domain Knowledge for Unseen Domain Generalization [arXiv 2021] (D2SDK, ensemble learning)

[175] Class-conditioned Domain Generalization via Wasserstein Distributional Robust Optimization [ICLR workshop 2021] (ensemble learning)

[176] Domain and Content Adaptive Convolution for Domain Generalization in Medical Image Segmentation [arXiv 2021] (DCAC, ensemble learning)

[177] Scale Invariant Domain Generalization Image Recapture Detection [ICONIP 2021] (SADG, domain alignment, self-supervised learning)

[178] Unsupervised Domain Generalization by Learning a Bridge Across Domains [arXiv 2021] (self-supervised learning, semi/weak/un-supervised domain generalization)

[179] Semi-Supervised Domain Generalization in RealWorld: New Benchmark and Strong Baseline [arXiv 2021] (domain alignment, semi/weak/un-supervised domain generalization)

[180] Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains [ICIP 2021] (x-EML, meta-learning)

[181] Energy-based Out-of-distribution Detection [NeurIPS 2020] [Code] (regularization)

[182] ROBIN : A Benchmark for Robustness to Individual Nuisances in Real-World Out-of-Distribution Shifts [arXiv 2021] (ROBIN dataset) [Code]

[183] Towards Non-I.I.D. Image Classification: A Dataset and Baselines [PR 2021] (NICO dataset)

[184] More is Better: A Novel Multi-view Framework for Domain Generalization [arXiv 2021] (data augmentation, meta-learning)

[185] Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation [ICIP 2021] (meta-learning, disentangled representation learning)

[186] Domain Generalization through Audio-Visual Relative Norm Alignment in First Person Action Recognition [WACV 2022] (RNA-Net, normalization)

[187] Generalizable Person Re-identification with Relevance-aware Mixture of Experts [CVPR 2021] (RaMoE, ensemble learning, person re-identification)

[188] Domain Generalization by Marginal Transfer Learning [JMLR 2021] [Code] (theory & analysis, data augmentation)

[189] Feature Alignment and Restoration for Domain Generalization and Adaptation [arXiv 2020] (FAR, domain alignment)

[190] Out-of-Distribution Generalization via Risk Extrapolation [ICML 2021] (REx, regularization)

[191] A Causal Framework for Distribution Generalization [TPAMI 2021] [Code] (NILE, causality)

[192] Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments [arXiv 2021] (domain alignment)

[193] Robustnet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening [CVPR 2021] [Code] (RobustNet, disentangled representation learning)

[194] Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder [ICCV 2021] (NSAE, self-supervised learning)

[195] Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference [IJCAI 2021] (VBCLS, domain alignment)

[196] The Risks of Invariant Risk Minimization [ICLR 2021] (theory & analysis)

[197] VideoDG: Generalizing Temporal Relations in Videos to Novel Domains [TPAMI 2021] [Code] (APN, data augmentation)

[198] An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers [NeurIPS 2021] [Code] (theory & analysis)

[199] Towards a Theoretical Framework of Out-Of-Distribution Generalization [NeurIPS 2021] (theory & analysis)

[200] Model-Based Domain Generalization [NeurIPS 2021] [Code] (MBDG, data augmentation, regularization)

[201] Swad: Domain Generalization by Seeking Flat Minima [NeurIPS 2021] [Code] (SWAD, regularization)

[202] Exploiting Domain-Specific Features to Enhance Domain Generalization [NeurIPS 2021] [Code] (mDSDI, meta-learning, disentangled representation learning, information)

[203] Adversarial Teacher-Student Representation Learning for Domain Generalization [NeurIPS 2021] (data augmentation, self-supervised learning)

[204] Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time [NeurIPS 2021] [Code] (GI, regularization)

[205] Out-of-Distribution Generalization in Kernel Regression [NeurIPS 2021] (theory & analysis)

[206] Quantifying and Improving Transferability in Domain Generalization [NeurIPS 2021] [Code] (theory & analysis, regularization)

[207] Invariance Principle Meets Information Bottleneck for Out-Of-Distribution Generalization [NeurIPS 2021] [Code] (IB-IRM, information, causality)

[208] TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification [NeurIPS 2021] [Code] (TransMatcher, ensemble learning, person re-identification)

Contributing & Contact

Feel free to contribute to our repository.

  • If you woulk like to correct mistakes, please do it directly;
  • If you would like to add/update papers, please finish the following tasks (if necessary):
    1. Update Paper Index.
    2. Update Papers.
    3. Update Datasets with reference of Paper Index.
  • If you have any questions or advice, please contact us by email (yuanjk@zju.edu.cn) or GitHub issues.

Thank you for your cooperation and contributions!

Acknowledgements