ashafahi / free_adv_trainLinks
Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural training.
☆175Updated last year
Alternatives and similar repositories for free_adv_train
Users that are interested in free_adv_train are comparing it to the libraries listed below
Sorting:
- PyTorch Implementation of Adversarial Training for Free!☆248Updated 4 years ago
- Datasets for the paper "Adversarial Examples are not Bugs, They Are Features"☆187Updated 4 years ago
- PyTorch library for adversarial attack and training☆146Updated 6 years ago
- Semisupervised learning for adversarial robustness https://arxiv.org/pdf/1905.13736.pdf☆142Updated 5 years ago
- Feature Scattering Adversarial Training (NeurIPS19)☆74Updated last year
- Further improve robustness of mixup-trained models in inference (ICLR 2020)☆60Updated 5 years ago
- Pytorch Adversarial Attack Framework☆78Updated 6 years ago
- Code for the CVPR 2019 article "Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses"☆136Updated 4 years ago
- Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, in ICCV 2019☆58Updated 5 years ago
- Max Mahalanobis Training (ICML 2018 + ICLR 2020)☆90Updated 4 years ago
- Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"☆227Updated 5 years ago
- Spatially Transformed Adversarial Examples with TensorFlow☆75Updated 6 years ago
- Analysis of Adversarial Logit Pairing☆60Updated 7 years ago
- Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs☆97Updated 4 years ago
- ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks☆169Updated 4 years ago
- ☆26Updated 6 years ago
- Code for "Robustness May Be at Odds with Accuracy"☆91Updated 2 years ago
- ☆157Updated 4 years ago
- Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019☆47Updated 2 years ago
- Adversarial Defense for Ensemble Models (ICML 2019)☆61Updated 4 years ago
- Understanding and Improving Fast Adversarial Training [NeurIPS 2020]☆95Updated 3 years ago
- Mitigating Adversarial Effects Through Randomization☆120Updated 7 years ago
- ☆88Updated last year
- CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is a robustness metric for deep neural networks☆62Updated 4 years ago
- Code for NeurIPS 2019 Paper☆47Updated 5 years ago
- Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.☆49Updated 5 years ago
- Smooth Adversarial Training☆68Updated 4 years ago
- Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"☆161Updated 5 years ago
- Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models (published in ICLR2018)☆243Updated 5 years ago
- Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing the…☆55Updated 2 years ago