out-of-distribution-generalization

There are 33 repositories under out-of-distribution-generalization topic.

  • thuml/Transfer-Learning-Library

    Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

    Language:Python3.5k49202559

  • huytransformer/Awesome-Out-Of-Distribution-Detection

    Out-of-distribution detection, robustness, and generalization resources. The repository contains a curated list of papers, tutorials, books, videos, articles and open-source libraries etc

    82837469

  • MinghuiChen43/awesome-trustworthy-deep-learning

    A curated list of trustworthy deep learning papers. Daily updating...

    34413034

  • divelab/GOOD

    GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]

    Language:Python18832619

  • THUMNLab/awesome-graph-ood

    Papers about out-of-distribution generalization on graphs.

    1581029

  • LFhase/CIGA

    [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

    Language:Python1034311

  • qitianwu/GraphOOD-EERM

    The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"

    Language:Python82339

  • pauljblazek/deepdistilling

    Mechanistically interpretable neurosymbolic AI (Nature Comput Sci 2024): losslessly compressing NNs to computer code and discovering new algorithms which generalize out-of-distribution and outperform human-designed algorithms

    Language:Python81417

  • qitianwu/GraphOOD-GNNSafe

    The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"

    Language:Python73455

  • yangnianzu0515/MoleOOD

    Official implementation for the paper "Learning Substructure Invariance for Out-of-Distribution Molecular Representations" (NeurIPS 2022).

    Language:Python60226

  • xuanlinli17/large_vlm_distillation_ood

    Distilling Large Vision-Language Model with Out-of-Distribution Generalizability (ICCV 2023)

    Language:Python54124

  • LFhase/PAIR

    [ICLR 2023, ICLR DG oral] PAIR, the optimizer and model selection criteria for OOD Generalization

    Language:Jupyter Notebook50104

  • kdhht2334/ELIM_FER

    [NeurIPS 2022] The official repository of Expression Learning with Identity Matching for Facial Expression Recognition

    Language:Python36164

  • joffery/TRO

    The Pytorch implementation for "Topology-aware Robust Optimization for Out-of-Distribution Generalization" (ICLR 2023)

    Language:Python31202

  • mala-lab/ADShift

    Official PyTorch implementation of the ICCV'23 paper “Anomaly Detection under Distribution Shift”

    Language:Python27164

  • qitianwu/IDCF

    Code for ICML21 spotlight paper "Towards open-world recommendation: An inductive model-based collaborative filtering approach"

    Language:Python25316

  • LFhase/FeAT

    [NeurIPS 2023] Understanding and Improving Feature Learning for Out-of-Distribution Generalization

    Language:Python24211

  • qitianwu/FATE

    Codes and datasets for NeurIPS21 paper “Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach”

    Language:Python23237

  • ForeverPs/PoER

    Potential energy ranking for domain generalization (DG)

    Language:Python19311

  • divelab/LECI

    The implementation of "Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization" (NeurIPS 2023)

    Language:Python18261

  • LFhase/GALA

    [NeurIPS 2023] Does Invariant Graph Learning via Environment Augmentation Learn Invariance?

    Language:Python18112

  • hosseinshn/Velodrome

    Velodrome combines semi-supervised learning and out-of-distribution generalization (domain generalization) for drug response prediction and pharmacogenomics

    Language:Python17212

  • juangamella/causal-chamber-paper

    Code to reproduce the case studies of the 2024 paper "The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology" by Juan L. Gamella, Jonas Peters and Peter Bühlmann.

    Language:Jupyter Notebook12100

  • tangli-udel/DRE

    The Pytorch implementation for "Are Data-driven Explanations Robust against Out-of-distribution Data?" (CVPR 2023)

    Language:Python9212

  • wondergo2017/sild

    Implementation codes for NeurIPS23 paper "Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts"

    Language:Python8201

  • deepmancer/rss-training-iclr2024

    This is the official repository for the ICLR 2024 paper Out-Of-Domain Unlabeled Data Improves Generalization.

    Language:TeX3100

  • etetteh/OoD_Gen-Chest_Xray

    Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models

    Language:Python3401

  • gnyanesh-bangaru/loss-analysis-cka

    This work is a analysis of representations acquired for standard, OOD and Biased data on numerous objective functions.

    Language:Jupyter Notebook3100

  • sophiewharrie/meta-learning-hierarchical-model-similar-causal-mechanisms

    Code for the research paper Meta-learning with hierarchical models based on similarity of causal mechanisms

    Language:Python2201

  • VirtuosoResearch/Distribution-Shifts-in-CMNIST

    Language:Python2100

  • bratjay01/Road-Seg

    Enhancing road segmentation model for Asphalt edge detection

    Language:Jupyter Notebook1100

  • wooks527/G-CPA

    GradCAM-based Copy and Paste Augmentation

    Language:Python1100

  • ZigeW/SODA

    [NeurIPS 2023] “SODA: Robust Training of Test-Time Data Adaptors”

    Language:Python0101