Alternatives and similar repositories for Understanding-Robust-Overfitting:

Users that are interested in Understanding-Robust-Overfitting are comparing it to the libraries listed below

• VITA-Group / Alleviate-Robust-Overfitting
  [ICLR 2021] "Robust Overfitting may be mitigated by properly learned smoothening" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chan…
  ☆46Updated 3 years ago
• zjfheart / Geometry-aware-Instance-reweighted-Adversarial-Training
  the paper "Geometry-aware Instance-reweighted Adversarial Training" ICLR 2021 oral
  ☆59Updated 3 years ago
• DequanWang / dent
  Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks
  ☆38Updated 3 years ago
• imrahulr / hat
  Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off
  ☆29Updated 2 years ago
• ChaojianYu / Robust-Weight-Perturbation
  Implementation for <Robust Weight Perturbation for Adversarial Training> in IJCAI'22.
  ☆14Updated 2 years ago
• goldblum / AdversariallyRobustDistillation
  Pytorch implementation of Adversarially Robust Distillation (ARD)
  ☆59Updated 5 years ago
• TLMichael / Delusive-Adversary
  [NeurIPS 2021] Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training
  ☆31Updated 3 years ago
• LijieFan / AdvCL
  [NeurIPS 2021] “When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?”
  ☆48Updated 3 years ago
• tml-epfl / adv-training-corruptions
  On the effectiveness of adversarial training against common corruptions [UAI 2022]
  ☆30Updated 2 years ago
• dvlab-research / LBGAT
  Learnable Boundary Guided Adversarial Training (ICCV2021)
  ☆36Updated last month
• val-iisc / GAMA-GAT
  Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses, NeurIPS Spotlight 2020
  ☆26Updated 4 years ago
• val-iisc / DAJAT
  Official Code for Efficient and Effective Augmentation Strategy for Adversarial Training (NeurIPS-2022)
  ☆16Updated last year
• fra31 / robust-finetuning
  Code relative to "Adversarial robustness against multiple and single $l_p$-threat models via quick fine-tuning of robust classifiers"
  ☆18Updated 2 years ago
• Saehyung-Lee / cifar10_challenge
  Code for the CVPR 2020 article "Adversarial Vertex mixup: Toward Better Adversarially Robust Generalization"
  ☆13Updated 4 years ago
• ShawnXYang / AT_HE
  ☆35Updated 4 years ago
• MotasemAlfarra / Combating-Adversaries-with-Anti-Adversaries
  ☆10Updated 3 years ago
• HanshuYAN / CIFS
  CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection
  ☆20Updated 3 years ago
• HanxunH / RobustWRN
  [NeurIPS2021] Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
  ☆34Updated 6 months ago
• kaiwenzha / contrastive-poisoning
  [ICLR 2023, Spotlight] Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning
  ☆30Updated last year
• xherdan76 / A-Unified-Approach-to-Interpreting-and-Boosting-Adversarial-Transferability
  A Unified Approach to Interpreting and Boosting Adversarial Transferability (ICLR2021)
  ☆29Updated 2 years ago
• dongyp13 / memorization-AT
  ☆21Updated 2 years ago
• val-iisc / NuAT
  Towards Efficient and Effective Adversarial Training, NeurIPS 2021
  ☆17Updated 2 years ago
• pdejorge / N-FGSM
  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
• jiaxiaojunQAQ / FGSM-PGI
  Code for Prior-Guided Adversarial Initialization for Fast Adversarial Training (ECCV2022)
  ☆24Updated 2 years ago
• PKU-ML / CFA
  ☆18Updated last year
• whj363636 / Self-Ensemble-Adversarial-Training
  SEAT
  ☆20Updated last year
• THUYimingLi / Semi-supervised_Robust_Training
  This is the code for semi-supervised robust training (SRT).
  ☆18Updated last year
• imrahulr / adversarial_robustness_pytorch
  Unofficial implementation of the DeepMind papers "Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples…
  ☆95Updated 2 years ago
• BrachioLab / adversarial_prompting
  ☆53Updated last year
• liuchen11 / AdversaryLossLandscape
  On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them [NeurIPS 2020]
  ☆35Updated 3 years ago