A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization
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Updated
Apr 28, 2024
A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization
RoboBEV: Towards Robust Bird's Eye View Perception under Common Corruption and Domain Shift
[ECCV 2022] Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization, and Beyond
[ICML 2023] Change is Hard: A Closer Look at Subpopulation Shift
Library for the training and evaluation of object-centric models (ICML 2022)
This repository contains the ViewFool and ImageNet-V proposed by the paper “ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints” (NeurIPS2022).
[NeurIPS 2022] "A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models", Yuanxin Liu, Fandong Meng, Zheng Lin, Jiangnan Li, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou
Implementation of the paper SAM-Deblur: Let Segment Anything Boost Image Deblurring(ICASSP2024)
Code for Mind the Label Shift of Augmentation-based Graph OOD generalization (LiSA) in CVPR 2023. LiSA is a model-agnostic Graph OOD framework.
Causal Representation Learning for Out-of-Distribution Recommendation (WWW'22)
The Limits of Fair Medical Imaging AI In The Wild
Masking Strategies for Background Bias Removal in Computer Vision Models (ICCVW OODCV 2023 paper)
The value of out-of-distribution data (ICML 2023)
Demographic Bias of Vision-Language Foundation Models in Medical Imaging
Causal Disentangled Recommendation Against Preference Shifts (TOIS), 2023
Code for the ICML 2021 paper "Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization" by Sang Michael Xie, Tengyu Ma, Percy Liang
[CVPR 2024] Improving out-of-distribution generalization in graphs via hierarchical semantic environments
[WACV 2024] Source code for "Consolidating separate degradations model via weights fusion and distillation".
Code for "Environment Diversification with Multi-head Neural Network for Invariant Learning" (NeurIPS 2022)
Code for the Conditional Mutual Information-Debiasing (CMID) method.
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