clustr-official-account / Rethinking-Clustering-for-Robustness
This is the official implementation of ClusTR: Clustering Training for Robustness paper.
☆20Updated 3 years ago
Alternatives and similar repositories for Rethinking-Clustering-for-Robustness:
Users that are interested in Rethinking-Clustering-for-Robustness are comparing it to the libraries listed below
- On the effectiveness of adversarial training against common corruptions [UAI 2022]☆30Updated 2 years ago
- ☆35Updated 4 years ago
- Coupling rejection strategy against adversarial attacks (CVPR 2022)☆29Updated 3 years ago
- Source code of "Hold me tight! Influence of discriminative features on deep network boundaries"☆22Updated 3 years ago
- Pytorch implementation of Adversarially Robust Distillation (ARD)☆59Updated 5 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
- Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2…☆22Updated 4 years ago
- On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them [NeurIPS 2020]☆36Updated 3 years ago
- Smooth Adversarial Training☆67Updated 4 years ago
- ☆8Updated 4 years ago
- Certified Patch Robustness via Smoothed Vision Transformers☆42Updated 3 years ago
- Understanding and Improving Fast Adversarial Training [NeurIPS 2020]☆96Updated 3 years ago
- ☆25Updated 4 years ago
- Codebase for "Exploring the Landscape of Spatial Robustness" (ICML'19, https://arxiv.org/abs/1712.02779).☆26Updated 5 years ago
- Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"☆34Updated 5 years ago
- Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".☆55Updated 3 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
- [NeurIPS 2020] "Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free" by Haotao Wang*, Tianlong C…☆44Updated 3 years ago
- Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses, NeurIPS Spotlight 2020☆27Updated 4 years ago
- Code for "Neuron Shapley: Discovering the Responsible Neurons"☆25Updated 11 months ago
- Official implementation of "Removing Batch Normalization Boosts Adversarial Training" (ICML'22)☆19Updated 2 years ago
- Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes☆23Updated 4 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- A Self-Consistent Robust Error (ICML 2022)☆67Updated last year
- Adversarially Robust Transfer Learning with LWF loss applied to the deep feature representation (penultimate) layer☆18Updated 5 years ago
- Data-free knowledge distillation using Gaussian noise (NeurIPS paper)☆15Updated 2 years ago
- Code for NeurIPS 2019 Paper☆48Updated 4 years ago
- Implementation for Jacobian Adversarially Regularized Networks for Robustness (ICLR 2020)☆21Updated 5 years ago
- ☆19Updated 3 years ago
- Code for the paper "SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness" (NeurIPS 2021)☆21Updated 2 years ago