yangarbiter / rare-spurious-correlationLinks
Understanding Rare Spurious Correlations in Neural Network
☆12Updated 3 years ago
Alternatives and similar repositories for rare-spurious-correlation
Users that are interested in rare-spurious-correlation are comparing it to the libraries listed below
Sorting:
- A modern look at the relationship between sharpness and generalization [ICML 2023]☆43Updated last year
- Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)☆13Updated 2 years ago
- Code for Environment Inference for Invariant Learning (ICML 2021 Paper)☆50Updated 4 years ago
- ☆14Updated 5 years ago
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆42Updated last year
- Test-Time Label-Shift Adaptation☆13Updated 2 years ago
- Distributional and Outlier Robust Optimization (ICML 2021)☆27Updated 3 years ago
- Code for the paper "Evading Black-box Classifiers Without Breaking Eggs" [SaTML 2024]☆20Updated last year
- LISA for ICML 2022☆49Updated 2 years ago
- Official code for "In Search of Robust Measures of Generalization" (NeurIPS 2020)☆28Updated 4 years ago
- ☆18Updated 2 years ago
- Code relative to "Adversarial robustness against multiple and single $l_p$-threat models via quick fine-tuning of robust classifiers"☆19Updated 2 years ago
- Code for "Just Train Twice: Improving Group Robustness without Training Group Information"☆72Updated last year
- ☆30Updated 3 years ago
- Towards Understanding Sharpness-Aware Minimization [ICML 2022]☆35Updated 3 years ago
- ☆12Updated last year
- On the effectiveness of adversarial training against common corruptions [UAI 2022]☆30Updated 3 years ago
- ☆24Updated 4 years ago
- Source code of "What can linearized neural networks actually say about generalization?☆20Updated 3 years ago
- ☆45Updated 2 years ago
- ☆22Updated 2 years ago
- ☆38Updated 4 years ago
- ☆8Updated 4 years ago
- Distilling Model Failures as Directions in Latent Space☆47Updated 2 years ago
- Official implementation for Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds (NeurIPS, 2021).☆24Updated 2 years ago
- Post-processing for fair classification☆15Updated 2 months ago
- ☆14Updated last year
- ☆34Updated last year
- Code and results accompanying our paper titled RLSbench: Domain Adaptation under Relaxed Label Shift☆34Updated last year
- [ICLR'22] Self-supervised learning optimally robust representations for domain shift.☆24Updated 3 years ago