Alternatives and similar repositories for PathwayGrad

Users that are interested in PathwayGrad are comparing it to the libraries listed below

• kai-wen-yang / CD-VAE
  [NeurIPS 2021] "Class-Disentanglement and Applications in Adversarial Detection and Defense"
  ☆46Updated 4 years ago
• tingxueronghua / pytorch-classification-advprop
  ☆107Updated 4 years ago
• zibojia / RSLAD
  This is the official code for "Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better"
  ☆45Updated 4 years ago
• tml-epfl / understanding-fast-adv-training
  Understanding and Improving Fast Adversarial Training [NeurIPS 2020]
  ☆96Updated 4 years ago
• LijieFan / AdvCL
  [NeurIPS 2021] “When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?”
  ☆48Updated 4 years ago
• Muzammal-Naseer / CDA
  Official repository for "Cross-Domain Transferability of Adversarial Perturbations" (NeurIPS 2019)
  ☆64Updated 4 years ago
• Muzammal-Naseer / NRP
  Official repository for "A Self-supervised Approach for Adversarial Robustness" (CVPR 2020--Oral)
  ☆100Updated 4 years ago
• aamir-mustafa / pcl-adversarial-defense
  Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, in ICCV 2019
  ☆58Updated 6 years ago
• goldblum / AdversariallyRobustDistillation
  Pytorch implementation of Adversarially Robust Distillation (ARD)
  ☆59Updated 6 years ago
• Muzammal-Naseer / TTP
  Official repository for "On Generating Transferable Targeted Perturbations" (ICCV 2021)
  ☆62Updated 2 years ago
• polo5 / ZeroShotKnowledgeTransfer
  Accompanying code for the paper "Zero-shot Knowledge Transfer via Adversarial Belief Matching"
  ☆144Updated 5 years ago
• CUAI / Intermediate-Level-Attack
  [ICCV 2019] Enhancing Adversarial Example Transferability with an Intermediate Level Attack (https://arxiv.org/abs/1907.10823)
  ☆80Updated 6 years ago
• A-LinCui / DenoisingNet_Adversarial_Training
  PyTorch implementation of "Feature Denoising for Improving Adversarial Robustness" on CIFAR10.
  ☆34Updated 5 years ago
• PKUAI26 / AT-CNN
  Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks
  ☆66Updated 6 years ago
• cychomatica / One-Pixel-Shotcut
  One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks (ICLR 2023 Spotlight)
  ☆14Updated 4 months ago
• AkhilanB / Proper-Interpretability
  Codes for reproducing the experimental results in "Proper Network Interpretability Helps Adversarial Robustness in Classification", publi…
  ☆13Updated 5 years ago
• VITA-Group / Adversarial-Contrastive-Learning
  [NeurIPS 2020] “ Robust Pre-Training by Adversarial Contrastive Learning”, Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang
  ☆116Updated 4 years ago
• YisenWang / MART
  Code for ICLR2020 "Improving Adversarial Robustness Requires Revisiting Misclassified Examples"
  ☆152Updated 5 years ago
• JIA-Lab-research / LBGAT
  Learnable Boundary Guided Adversarial Training (ICCV2021)
  ☆38Updated last year
• cvlab-columbia / SelfSupDefense
  ☆30Updated 3 years ago
• HanshuYAN / CIFS
  CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection
  ☆20Updated 4 years ago
• bymavis / CAS_ICLR2021
  ☆58Updated 3 years ago
• princetonvisualai / DomainBiasMitigation
  ☆72Updated 5 years ago
• P2333 / Mixup-Inference
  Further improve robustness of mixup-trained models in inference (ICLR 2020)
  ☆60Updated 5 years ago
• ShawnXYang / AT_HE
  ☆35Updated 5 years ago
• columbia / Metric_Learning_Adversarial_Robustness
  Code for NeurIPS 2019 Paper
  ☆48Updated 5 years ago
• elliothe / CVPR_2019_PNI
  pytorch implementation of Parametric Noise Injection for adversarial defense
  ☆46Updated 6 years ago
• wjyouch / bilateral-adversarial-training
  ☆16Updated 6 years ago
• hongw579 / AGKD-BML
  Codes for ICCV 2021 paper "AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Met…
  ☆12Updated 3 years ago
• VITA-Group / Adv-SS-Pretraining
  [CVPR 2020] Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
  ☆85Updated 4 years ago