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
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Updated
Nov 21, 2024
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
Official PyTorch implementation of Fully Attentional Networks
ImageNet-R(endition) and DeepAugment (ICCV 2021)
[ICLR 2022] Official pytorch implementation of "Uncertainty Modeling for Out-of-Distribution Generalization" in International Conference on Learning Representations (ICLR) 2022.
[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.
[ICML 2023] Change is Hard: A Closer Look at Subpopulation Shift
[KDD 2022] "Causal Attention for Interpretable and Generalizable Graph Classification" by Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, Xiangnan He, Tat-Seng Chua.
A project to add scalable state-of-the-art out-of-distribution detection (open set recognition) support by changing two lines of code! Perform efficient inferences (i.e., do not increase inference time) and detection without classification accuracy drop, hyperparameter tuning, or collecting additional data.
The Official Implementation of the ICCV-2021 Paper: Semantically Coherent Out-of-Distribution Detection.
OOD Generalization and Detection (ACL 2020)
[ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"
Distilling Large Vision-Language Model with Out-of-Distribution Generalizability (ICCV 2023)
Official repository of STONE (KDD 2024)
A project to improve out-of-distribution detection (open set recognition) and uncertainty estimation by changing a few lines of code in your project! Perform efficient inferences (i.e., do not increase inference time) without repetitive model training, hyperparameter tuning, or collecting additional data.
[CVPR 2024 Highlight] ImageNet-D
[MICCAI 2022 Best Paper Finalist] Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi Supervised Segmentation
Official code and data for NeurIPS 2023 paper "ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification"
[CVPR 2023] Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection
[Nature Medicine] The Limits of Fair Medical Imaging AI In Real-World Generalization
Implementation and Benchmark Splits to study Out-of-Distribution Generalization in Deep Metric Learning.
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