wwoods / adversarial-explanations-cifarLinks
Code example for the paper, "Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness."
☆23Updated last year
Alternatives and similar repositories for adversarial-explanations-cifar
Users that are interested in adversarial-explanations-cifar are comparing it to the libraries listed below
Sorting:
- Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, in ICCV 2019☆58Updated 5 years ago
- Accompanying code for the paper "Zero-shot Knowledge Transfer via Adversarial Belief Matching"☆143Updated 5 years ago
- Full-gradient saliency maps☆212Updated 2 years ago
- Information Bottlenecks for Attribution☆82Updated 2 years ago
- Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"☆162Updated 5 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- Concept activation vectors for Keras☆14Updated 2 years ago
- 'Robust Semantic Interpretability: Revisiting Concept Activation Vectors' Official Implementation☆11Updated 5 years ago
- Max Mahalanobis Training (ICML 2018 + ICLR 2020)☆90Updated 4 years ago
- Python implementation for evaluating explanations presented in "On the (In)fidelity and Sensitivity for Explanations" in NeurIPS 2019 for…☆25Updated 3 years ago
- Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks☆66Updated 6 years ago
- Figures & code from the paper "Shortcut Learning in Deep Neural Networks" (Nature Machine Intelligence 2020)☆101Updated 3 years ago
- ☆51Updated 5 years ago
- Pytorch implementation of Real Time Image Saliency for Black Box Classifiers https://arxiv.org/abs/1705.07857☆59Updated 5 years ago
- SmoothGrad implementation in PyTorch☆172Updated 4 years ago
- ☆26Updated 6 years ago
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆128Updated 4 years ago
- Towards Automatic Concept-based Explanations☆160Updated last year
- Trained model weights, training and evaluation code from the paper "A simple way to make neural networks robust against diverse image cor…☆62Updated 2 years ago
- Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural trai…☆175Updated last year
- Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing the…☆55Updated 2 years ago
- REPresentAtion bIas Removal (REPAIR) of datasets☆56Updated 2 years ago
- Code for our CVPR 2018 paper, "On the Robustness of Semantic Segmentation Models to Adversarial Attacks"☆103Updated 6 years ago
- Further improve robustness of mixup-trained models in inference (ICLR 2020)☆60Updated 5 years ago
- Code for the Paper 'On the Connection Between Adversarial Robustness and Saliency Map Interpretability' by C. Etmann, S. Lunz, P. Maass, …☆16Updated 6 years ago
- Pytorch Adversarial Attack Framework☆78Updated 6 years ago
- Reverse Cross Entropy for Adversarial Detection (NeurIPS 2018)☆47Updated 4 years ago
- Explaining Image Classifiers by Counterfactual Generation☆28Updated 3 years ago
- Code for NeurIPS 2019 Paper☆47Updated 5 years ago
- PyTorch implementation of "Feature Denoising for Improving Adversarial Robustness" on CIFAR10.☆35Updated 5 years ago