Alternatives and similar repositories for YOPO-You-Only-Propagate-Once

Users that are interested in YOPO-You-Only-Propagate-Once are comparing it to the libraries listed below

• MadryLab / robust_representations
  Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"
  ☆160Updated 5 years ago
• MadryLab / constructed-datasets
  Datasets for the paper "Adversarial Examples are not Bugs, They Are Features"
  ☆187Updated 4 years ago
• MadryLab / robust-features-code
  Code for "Robustness May Be at Odds with Accuracy"
  ☆91Updated 2 years ago
• jeromerony / fast_adversarial
  Code for the CVPR 2019 article "Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses"
  ☆135Updated 4 years ago
• ashafahi / free_adv_train
  Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural trai…
  ☆174Updated last year
• wanglouis49 / pytorch-adversarial_box
  PyTorch library for adversarial attack and training
  ☆145Updated 6 years ago
• mahyarnajibi / FreeAdversarialTraining
  PyTorch Implementation of Adversarial Training for Free!
  ☆246Updated 3 years ago
• huanzhang12 / Adversarial_Survey
  Robustness vs Accuracy Survey on ImageNet
  ☆98Updated 3 years ago
• labsix / adversarial-logit-pairing-analysis
  Analysis of Adversarial Logit Pairing
  ☆60Updated 6 years ago
• yaircarmon / semisup-adv
  Semisupervised learning for adversarial robustness https://arxiv.org/pdf/1905.13736.pdf
  ☆142Updated 5 years ago
• MadryLab / robustness_applications
  Notebooks for reproducing the paper "Computer Vision with a Single (Robust) Classifier"
  ☆128Updated 5 years ago
• gmh14 / RobNets
  [CVPR 2020] When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks
  ☆124Updated 4 years ago
• P2333 / Mixup-Inference
  Further improve robustness of mixup-trained models in inference (ICLR 2020)
  ☆60Updated 4 years ago
• Hadisalman / smoothing-adversarial
  Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"
  ☆225Updated 5 years ago
• locuslab / projected_sinkhorn
  ☆87Updated 10 months ago
• P2333 / Max-Mahalanobis-Training
  Max Mahalanobis Training (ICML 2018 + ICLR 2020)
  ☆90Updated 4 years ago
• microsoft / denoised-smoothing
  Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs
  ☆97Updated 4 years ago
• evaluating-adversarial-robustness / adv-eval-paper
  LaTeX source for the paper "On Evaluating Adversarial Robustness"
  ☆255Updated 4 years ago
• rwightman / pytorch-nips2017-attack-example
  A PyTorch baseline attack example for the NIPS 2017 adversarial competition
  ☆85Updated 7 years ago
• fra31 / fab-attack
  Code for FAB-attack
  ☆33Updated 4 years ago
• revbucket / mister_ed
  Pytorch Adversarial Attack Framework
  ☆78Updated 6 years ago
• cihangxie / NIPS2017_adv_challenge_defense
  Mitigating Adversarial Effects Through Randomization
  ☆120Updated 7 years ago
• aamir-mustafa / pcl-adversarial-defense
  Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, in ICCV 2019
  ☆58Updated 5 years ago
• PKUAI26 / AT-CNN
  Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks
  ☆66Updated 5 years ago
• P2333 / Bag-of-Tricks-for-AT
  Empirical tricks for training robust models (ICLR 2021)
  ☆253Updated 2 years ago
• kkew3 / pytorch-cw2
  A rich-documented PyTorch implementation of Carlini-Wagner's L2 attack.
  ☆60Updated 6 years ago
• cihangxie / DI-2-FGSM
  Improving Transferability of Adversarial Examples with Input Diversity
  ☆164Updated 6 years ago
• karandwivedi42 / adversarial
  Pytorch - Adversarial Training
  ☆26Updated 7 years ago
• yeshaokai / Robustness-Aware-Pruning-ADMM
  Code release for "Adversarial Robustness vs Model Compression, or Both?"
  ☆91Updated 3 years ago
• locuslab / robust_overfitting
  ☆157Updated 4 years ago