Awesome Incremental Learning / Lifelong learning

Survey

  • Online Continual Learning in Image Classification: An Empirical Survey (Neurocomputing 2021) [paper] [code]
  • Continual Lifelong Learning in Natural Language Processing: A Survey (COLING 2020) [paper]
  • Class-incremental learning: survey and performance evaluation (arXiv 2020) [paper] [code]
  • A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (Neural Networks) [paper] [code]
  • A continual learning survey: Defying forgetting in classification tasks (TPAMI 2021) [paper] [arxiv]
  • Continual Lifelong Learning with Neural Networks: A Review (Neural Networks) [paper]
  • Three scenarios for continual learning (arXiv 2019) [paper][code]

Papers

2022

  • Learngene: From Open-World to Your Learning Task (AAAI2022) [paper]
  • Rethinking the Representational Continuity: Towards Unsupervised Continual Learning (ICLR2022) [paper]
  • Continual Learning with Filter Atom Swapping (ICLR2022) [paper]
  • Continual Learning with Recursive Gradient Optimization (ICLR2022) [paper]
  • TRGP: Trust Region Gradient Projection for Continual Learning (ICLR2022) [paper]
  • Looking Back on Learned Experiences For Class/task Incremental Learning (ICLR2022) [paper]
  • Continual Normalization: Rethinking Batch Normalization for Online Continual Learning (ICLR2022) [paper]
  • Model Zoo: A Growing Brain That Learns Continually (ICLR2022) [paper]
  • Learning curves for continual learning in neural networks: Self-knowledge transfer and forgetting (ICLR2022) [paper]
  • Memory Replay with Data Compression for Continual Learning (ICLR2022) [paper]
  • Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System (ICLR2022) [paper]
  • Online Coreset Selection for Rehearsal-based Continual Learning (ICLR2022) [paper]
  • Pretrained Language Model in Continual Learning: A Comparative Study (ICLR2022) [paper]
  • Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference (ICLR2022) [paper]
  • New Insights on Reducing Abrupt Representation Change in Online Continual Learning (ICLR2022) [paper]
  • Towards Continual Knowledge Learning of Language Models (ICLR2022) [paper]
  • CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability (ICLR2022) [paper]
  • CoMPS: Continual Meta Policy Search (ICLR2022) [paper]
  • Information-theoretic Online Memory Selection for Continual Learning (ICLR2022) [paper]
  • Subspace Regularizers for Few-Shot Class Incremental Learning (ICLR2022) [paper]
  • LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5 (ICLR2022) [paper]
  • Effect of scale on catastrophic forgetting in neural networks (ICLR2022) [paper]

2021

  • Incremental Object Detection via Meta-Learning (TPAMI 2021) [paper] [code]
  • Class-Incremental Learning via Dual Augmentation (NeurIPS2021) [paper]
  • SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning (NeurIPS2021) [paper]
  • RMM: Reinforced Memory Management for Class-Incremental Learning (NeurIPS2021) [paper]
  • Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima (NeurIPS2021) [paper]
  • Lifelong Domain Adaptation via Consolidated Internal Distribution (NeurIPS2021) [paper]
  • AFEC: Active Forgetting of Negative Transfer in Continual Learning (NeurIPS2021) [paper]
  • Natural continual learning: success is a journey, not (just) a destination (NeurIPS2021) [paper]
  • Gradient-based Editing of Memory Examples for Online Task-free Continual Learning (NeurIPS2021) [paper]
  • Optimizing Reusable Knowledge for Continual Learning via Metalearning (NeurIPS2021) [paper]
  • Formalizing the Generalization-Forgetting Trade-off in Continual Learning (NeurIPS2021) [paper]
  • Learning where to learn: Gradient sparsity in meta and continual learning (NeurIPS2021) [paper]
  • Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning (NeurIPS2021) [paper]
  • Posterior Meta-Replay for Continual Learning (NeurIPS2021) [paper]
  • Continual Auxiliary Task Learning (NeurIPS2021) [paper]
  • Mitigating Forgetting in Online Continual Learning with Neuron Calibration (NeurIPS2021) [paper]
  • BNS: Building Network Structures Dynamically for Continual Learning (NeurIPS2021) [paper]
  • DualNet: Continual Learning, Fast and Slow (NeurIPS2021) [paper]
  • BooVAE: Boosting Approach for Continual Learning of VAE (NeurIPS2021) [paper]
  • Generative vs. Discriminative: Rethinking The Meta-Continual Learning (NeurIPS2021) [paper]
  • Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning (NeurIPS2021) [paper]
  • Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection (NeurIPS, 2021) [paper]
  • SS-IL: Separated Softmax for Incremental Learning (ICCV, 2021) [paper]
  • Striking a Balance between Stability and Plasticity for Class-Incremental Learning (ICCV, 2021) [paper]
  • Synthesized Feature based Few-Shot Class-Incremental Learning on a Mixture of Subspaces (ICCV, 2021) [paper]
  • Class-Incremental Learning for Action Recognition in Videos (ICCV, 2021) [paper]
  • Continual Prototype Evolution:Learning Online from Non-Stationary Data Streams (ICCV, 2021) [paper]
  • Rehearsal Revealed: The Limits and Merits of Revisiting Samples in Continual Learning (ICCV, 2021) [paper]
  • Co2L: Contrastive Continual Learning (ICCV, 2021) [paper]
  • Wanderlust: Online Continual Object Detection in the Real World (ICCV, 2021) [paper]
  • Continual Learning on Noisy Data Streams via Self-Purified Replay (ICCV, 2021) [paper]
  • Else-Net: Elastic Semantic Network for Continual Action Recognition from Skeleton Data (ICCV, 2021) [paper]
  • Detection and Continual Learning of Novel Face Presentation Attacks (ICCV, 2021) [paper]
  • Online Continual Learning with Natural Distribution Shifts: An Empirical Study with Visual Data (ICCV, 2021) [paper]
  • Continual Learning for Image-Based Camera Localization (ICCV, 2021) [paper]
  • Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting (ICCV, 2021) [paper]
  • Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning (ICCV, 2021) [paper]
  • RECALL: Replay-based Continual Learning in Semantic Segmentation (ICCV, 2021) [paper]
  • Few-Shot and Continual Learning with Attentive Independent Mechanisms (ICCV, 2021) [paper]
  • Learning with Selective Forgetting (IJCAI, 2021) [paper]
  • Continuous Coordination As a Realistic Scenario for Lifelong Learning (ICML, 2021) [paper]
  • Kernel Continual Learning (ICML, 2021) [paper]
  • Variational Auto-Regressive Gaussian Processes for Continual Learning (ICML, 2021) [paper]
  • Bayesian Structural Adaptation for Continual Learning (ICML, 2021) [paper]
  • Continual Learning in the Teacher-Student Setup: Impact of Task Similarity (ICML, 2021) [paper]
  • Continuous Coordination As a Realistic Scenario for Lifelong Learning (ICML, 2021) [paper]
  • Federated Continual Learning with Weighted Inter-client Transfer (ICML, 2021) [paper]
  • Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks (NAACL, 2021) [paper]
  • Continual Learning for Text Classification with Information Disentanglement Based Regularization (NAACL, 2021) [paper]
  • CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks (EMNLP, 2021) [paper][code]
  • Co-Transport for Class-Incremental Learning (ACM MM, 2021) [paper]
  • Towards Open World Object Detection (CVPR, 2021) [paper] [code] [video]
  • Prototype Augmentation and Self-Supervision for Incremental Learning (CVPR, 2021) [paper]
  • ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning (CVPR, 2021) [paper]
  • Incremental Learning via Rate Reduction (CVPR, 2021) [paper]
  • IIRC: Incremental Implicitly-Refined Classification (CVPR, 2021) [paper]
  • Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning (CVPR, 2021) [paper]
  • Image De-raining via Continual Learning (CVPR, 2021) [paper]
  • Continual Learning via Bit-Level Information Preserving (CVPR, 2021) [paper]
  • Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned Generation (CVPR, 2021) [paper]
  • Lifelong Person Re-Identification via Adaptive Knowledge Accumulation (CVPR, 2021) [paper]
  • Distilling Causal Effect of Data in Class-Incremental Learning (CVPR, 2021) [paper]
  • Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning (CVPR, 2021) [paper]
  • Layerwise Optimization by Gradient Decomposition for Continual Learning (CVPR, 2021) [paper]
  • Adaptive Aggregation Networks for Class-Incremental Learning (CVPR, 2021) [paper]
  • Incremental Few-Shot Instance Segmentation (CVPR, 2021) [paper]
  • Efficient Feature Transformations for Discriminative and Generative Continual Learning (CVPR, 2021) [paper]
  • On Learning the Geodesic Path for Incremental Learning (CVPR, 2021) [paper]
  • Few-Shot Incremental Learning with Continually Evolved Classifiers (CVPR, 2021) [paper]
  • Rectification-based Knowledge Retention for Continual Learning (CVPR, 2021) [paper]
  • DER: Dynamically Expandable Representation for Class Incremental Learning (CVPR, 2021) [paper]
  • Rainbow Memory: Continual Learning with a Memory of Diverse Samples (CVPR, 2021) [paper]
  • Training Networks in Null Space of Feature Covariance for Continual Learning (CVPR, 2021) [paper]
  • Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning (CVPR, 2021) [paper]
  • PLOP: Learning without Forgetting for Continual Semantic Segmentation (CVPR, 2021) [paper]
  • Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations (CVPR, 2021) [paper]
  • Online Class-Incremental Continual Learning with Adversarial Shapley Value(AAAI, 2021) [paper] [code]
  • Lifelong and Continual Learning Dialogue Systems: Learning during Conversation(AAAI, 2021) [paper]
  • Continual learning for named entity recognition(AAAI, 2021) [paper]
  • Using Hindsight to Anchor Past Knowledge in Continual Learning(AAAI, 2021) [paper]
  • Curriculum-Meta Learning for Order-Robust Continual Relation Extraction(AAAI, 2021) [paper]
  • Continual Learning by Using Information of Each Class Holistically(AAAI, 2021) [paper]
  • Gradient Regularized Contrastive Learning for Continual Domain Adaptation(AAAI, 2021) [paper]
  • Unsupervised Model Adaptation for Continual Semantic Segmentation(AAAI, 2021) [paper]
  • A Continual Learning Framework for Uncertainty-Aware Interactive Image Segmentation(AAAI, 2021) [paper]
  • Do Not Forget to Attend to Uncertainty While Mitigating Catastrophic Forgetting(WACV, 2021) [paper]

2020

  • Rethinking Experience Replay: a Bag of Tricks for Continual Learning(ICPR, 2020) [paper] [code]
  • Continual Learning for Natural Language Generation in Task-oriented Dialog Systems(EMNLP, 2020) [paper]
  • Distill and Replay for Continual Language Learning(COLING, 2020) [paper]
  • Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks (NeurIPS2020) [paper] [code]
  • Meta-Consolidation for Continual Learning (NeurIPS2020) [paper]
  • Understanding the Role of Training Regimes in Continual Learning (NeurIPS2020) [paper]
  • Continual Learning with Node-Importance based Adaptive Group Sparse Regularization (NeurIPS2020) [paper]
  • Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning (NeurIPS2020) [paper]
  • Coresets via Bilevel Optimization for Continual Learning and Streaming (NeurIPS2020) [paper]
  • RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning (NeurIPS2020) [paper]
  • Continual Deep Learning by Functional Regularisation of Memorable Past (NeurIPS2020) [paper]
  • Dark Experience for General Continual Learning: a Strong, Simple Baseline (NeurIPS2020) [paper] [code]
  • GAN Memory with No Forgetting (NeurIPS2020) [paper]
  • Calibrating CNNs for Lifelong Learning (NeurIPS2020) [paper]
  • Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization (NeurIPS2020) [paper]
  • ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation(RecSys, 2020) [paper]
  • Initial Classifier Weights Replay for Memoryless Class Incremental Learning (BMVC2020) [paper]
  • Adversarial Continual Learning (ECCV2020) [paper] [code]
  • REMIND Your Neural Network to Prevent Catastrophic Forgetting (ECCV2020) [paper] [code]
  • Incremental Meta-Learning via Indirect Discriminant Alignment (ECCV2020) [paper]
  • Memory-Efficient Incremental Learning Through Feature Adaptation (ECCV2020) [paper]
  • PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning (ECCV2020) [paper] [code]
  • Reparameterizing Convolutions for Incremental Multi-Task Learning Without Task Interference (ECCV2020) [paper]
  • Learning latent representions across multiple data domains using Lifelong VAEGAN (ECCV2020) [paper]
  • Online Continual Learning under Extreme Memory Constraints (ECCV2020) [paper]
  • Class-Incremental Domain Adaptation (ECCV2020) [paper]
  • More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning (ECCV2020) [paper]
  • Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation (ECCV2020) [paper]
  • GDumb: A Simple Approach that Questions Our Progress in Continual Learning (ECCV2020) [paper]
  • Imbalanced Continual Learning with Partitioning Reservoir Sampling (ECCV2020) [paper]
  • Topology-Preserving Class-Incremental Learning (ECCV2020) [paper]
  • GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems (CIKM2020) [paper]
  • OvA-INN: Continual Learning with Invertible Neural Networks (IJCNN2020) [paper]
  • XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning (ICLM2020) [paper]
  • Optimal Continual Learning has Perfect Memory and is NP-HARD (ICML2020) [paper]
  • Neural Topic Modeling with Continual Lifelong Learning (ICML2020) [paper]
  • Continual Learning with Knowledge Transfer for Sentiment Classification (ECML-PKDD2020) [paper] [code]
  • Semantic Drift Compensation for Class-Incremental Learning (CVPR2020) [paper] [code]
  • Few-Shot Class-Incremental Learning (CVPR2020) [paper]
  • Modeling the Background for Incremental Learning in Semantic Segmentation (CVPR2020) [paper]
  • Incremental Few-Shot Object Detection (CVPR2020) [paper]
  • Incremental Learning In Online Scenario (CVPR2020) [paper]
  • Maintaining Discrimination and Fairness in Class Incremental Learning (CVPR2020) [paper]
  • Conditional Channel Gated Networks for Task-Aware Continual Learning (CVPR2020) [paper]
  • Continual Learning with Extended Kronecker-factored Approximate Curvature (CVPR2020) [paper]
  • iTAML : An Incremental Task-Agnostic Meta-learning Approach (CVPR2020) [paper] [code]
  • Mnemonics Training: Multi-Class Incremental Learning without Forgetting (CVPR2020) [paper] [code]
  • ScaIL: Classifier Weights Scaling for Class Incremental Learning (WACV2020) [paper]
  • Accepted papers(ICLR2020) [paper]
  • Brain-inspired replay for continual learning with artificial neural networks (Natrue Communications 2020) [paper] [code]
  • Learning to Continually Learn (ECAI 2020) [paper] [code]

2019

  • Compacting, Picking and Growing for Unforgetting Continual Learning (NeurIPS2019)[paper][code]
  • Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning (ICMR2019) [paper][code]
  • Towards Training Recurrent Neural Networks for Lifelong Learning (Neural Computation 2019) [paper]
  • Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay (IJCAI2019) [paper]
  • IL2M: Class Incremental Learning With Dual Memory (ICCV2019) [paper]
  • Incremental Learning Using Conditional Adversarial Networks (ICCV2019) [paper]
  • Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability (KDD2019) [paper]
  • Random Path Selection for Incremental Learning (NeurIPS2019) [paper]
  • Online Continual Learning with Maximal Interfered Retrieval (NeurIPS2019) [paper]
  • Meta-Learning Representations for Continual Learning (NeurIPS2019) [paper] [code]
  • Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild (ICCV2019) [paper]
  • Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation (ICCV2019) [paper]
  • Lifelong GAN: Continual Learning for Conditional Image Generation (ICCV2019) [paper]
  • Continual learning of context-dependent processing in neural networks (Nature Machine Intelligence 2019) [paper] [code]
  • Large Scale Incremental Learning (CVPR2019) [paper] [code]
  • Learning a Unified Classifier Incrementally via Rebalancing (CVPR2019) [paper] [code]
  • Learning Without Memorizing (CVPR2019) [paper]
  • Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning (CVPR2019) [paper]
  • Task-Free Continual Learning (CVPR2019) [paper]
  • Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting (ICML2019) [paper]
  • Efficient Lifelong Learning with A-GEM (ICLR2019) [paper] [code]
  • Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference (ICLR2019) [paper] [code]
  • Overcoming Catastrophic Forgetting via Model Adaptation (ICLR2019) [paper]
  • A comprehensive, application-oriented study of catastrophic forgetting in DNNs (ICLR2019) [paper]

2018

  • Memory Replay GANs: learning to generate images from new categories without forgetting (NIPS2018) [paper] [code]
  • Reinforced Continual Learning (NIPS2018) [paper] [code]
  • Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting (NIPS2018) [paper]
  • Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting (R-EWC) (ICPR2018) [paper] [code]
  • Exemplar-Supported Generative Reproduction for Class Incremental Learning (BMVC2018) [paper] [code]
  • End-to-End Incremental Learning (ECCV2018) [paper][code]
  • Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence (ECCV2018)[paper]
  • Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights (ECCV2018) [paper] [code]
  • Memory Aware Synapses: Learning what (not) to forget (ECCV2018) [paper] [code]
  • Lifelong Learning via Progressive Distillation and Retrospection (ECCV2018) [paper]
  • PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning (CVPR2018) [paper] [code]
  • Overcoming Catastrophic Forgetting with Hard Attention to the Task (ICML2018) [paper] [code]
  • Lifelong Learning with Dynamically Expandable Networks (ICLR2018) [paper]
  • FearNet: Brain-Inspired Model for Incremental Learning (ICLR2018) [paper]

2017

  • Incremental Learning of Object Detectors Without Catastrophic Forgetting (ICCV2017) [paper]
  • Overcoming catastrophic forgetting in neural networks (EWC) (PNAS2017) [paper] [code] [code]
  • Continual Learning Through Synaptic Intelligence (ICML2017) [paper] [code]
  • Gradient Episodic Memory for Continual Learning (NIPS2017) [paper] [code]
  • iCaRL: Incremental Classifier and Representation Learning (CVPR2017) [paper] [code]
  • Continual Learning with Deep Generative Replay (NIPS2017) [paper] [code]
  • Overcoming Catastrophic Forgetting by Incremental Moment Matching (NIPS2017) [paper] [code]
  • Expert Gate: Lifelong Learning with a Network of Experts (CVPR2017) [paper]
  • Encoder Based Lifelong Learning (ICCV2017) [paper]

2016

  • Learning without forgetting (ECCV2016) [paper] [code]

Find it interesting that there are more shared techniques than I thought for incremental learning (exemplars-based).

ContinualAI wiki

Workshops

Challenges or Competitions

Feel free to contact me if you find any interesting paper is missing.

Workshop papers are currently out due to space.