j-cb / Breaking_Down_OOD_Detection
☆11Updated 2 months ago
Alternatives and similar repositories for Breaking_Down_OOD_Detection:
Users that are interested in Breaking_Down_OOD_Detection are comparing it to the libraries listed below
- Provable Worst Case Guarantees for the Detection of Out-of-Distribution Data☆13Updated 2 years ago
- Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2…☆22Updated 4 years ago
- ☆23Updated 2 years ago
- Official repo for the paper "Make Some Noise: Reliable and Efficient Single-Step Adversarial Training" (https://arxiv.org/abs/2202.01181)☆25Updated 2 years ago
- Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)☆13Updated 2 years ago
- ☆21Updated 4 years ago
- On the effectiveness of adversarial training against common corruptions [UAI 2022]☆30Updated 2 years ago
- Understanding Rare Spurious Correlations in Neural Network☆12Updated 2 years ago
- A modern look at the relationship between sharpness and generalization [ICML 2023]☆43Updated last year
- A way to achieve uniform confidence far away from the training data.☆38Updated 4 years ago
- Code relative to "Adversarial robustness against multiple and single $l_p$-threat models via quick fine-tuning of robust classifiers"☆18Updated 2 years ago
- [ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models.☆26Updated 9 months ago
- Code release for the ICML 2019 paper "Are generative classifiers more robust to adversarial attacks?"☆23Updated 5 years ago
- Implementation for <Understanding Robust Overftting of Adversarial Training and Beyond> in ICML'22.☆12Updated 2 years ago
- ☆45Updated 2 years ago
- [NeurIPS 2021] “When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?”☆48Updated 3 years ago
- Pytorch implementation of Adversarially Robust Distillation (ARD)☆59Updated 5 years ago
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆42Updated last year
- Implementation for Jacobian Adversarially Regularized Networks for Robustness (ICLR 2020)☆21Updated 5 years ago
- Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning. (Neurips 2021)☆8Updated 3 years ago
- ☆12Updated 2 years ago
- Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks☆13Updated 3 years ago
- Implementation of Confidence-Calibrated Adversarial Training (CCAT).☆45Updated 4 years ago
- ☆24Updated 3 years ago
- On the Importance of Gradients for Detecting Distributional Shifts in the Wild☆56Updated 2 years ago
- Code for "Just Train Twice: Improving Group Robustness without Training Group Information"☆71Updated 11 months ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- ☆54Updated 4 years ago
- kyleliang919 / Uncovering-the-Connections-BetweenAdversarial-Transferability-and-Knowledge-Transferabilitycode for ICML 2021 paper in which we explore the relationship between adversarial transferability and knowledge transferability.☆17Updated 2 years ago
- ☆46Updated 4 years ago