MadryLab / BREEDS-BenchmarksLinks
☆55Updated 4 years ago
Alternatives and similar repositories for BREEDS-Benchmarks
Users that are interested in BREEDS-Benchmarks are comparing it to the libraries listed below
Sorting:
- The Pitfalls of Simplicity Bias in Neural Networks [NeurIPS 2020] (http://arxiv.org/abs/2006.07710v2)☆41Updated last year
- ☆62Updated 4 years ago
- ☆38Updated 4 years ago
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆42Updated last year
- Training vision models with full-batch gradient descent and regularization☆37Updated 2 years ago
- ☆34Updated 3 years ago
- An Investigation of Why Overparameterization Exacerbates Spurious Correlations☆31Updated 4 years ago
- A modern look at the relationship between sharpness and generalization [ICML 2023]☆43Updated last year
- Code for the ICLR 2022 paper. Salient Imagenet: How to discover spurious features in deep learning?☆40Updated 2 years ago
- Code for the paper "Understanding Generalization through Visualizations"☆61Updated 4 years ago
- ICLR 2021, Fair Mixup: Fairness via Interpolation☆56Updated 3 years ago
- Towards Understanding Sharpness-Aware Minimization [ICML 2022]☆35Updated 3 years ago
- Toy datasets to evaluate algorithms for domain generalization and invariance learning.☆42Updated 3 years ago
- Gradient Starvation: A Learning Proclivity in Neural Networks☆61Updated 4 years ago
- ☆34Updated 4 years ago
- Crowdsourcing metrics and test datasets beyond ImageNet (ICML 2022 workshop)☆37Updated last year
- A Closer Look at Accuracy vs. Robustness☆89Updated 4 years ago
- Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)☆13Updated 2 years ago
- [ICLR'22] Self-supervised learning optimally robust representations for domain shift.☆24Updated 3 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- ☆35Updated last year
- Code for "Just Train Twice: Improving Group Robustness without Training Group Information"☆72Updated last year
- Code for Active Learning at The ImageNet Scale. This repository implements many popular active learning algorithms and allows training wi…☆53Updated 3 years ago
- ☆50Updated 2 years ago
- A way to achieve uniform confidence far away from the training data.☆38Updated 4 years ago
- ☆95Updated 2 years ago
- [ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models.☆26Updated 11 months ago
- ☆34Updated last year
- ☆45Updated 2 years ago
- Weight-Averaged Sharpness-Aware Minimization (NeurIPS 2022)☆28Updated 2 years ago