jessemzhang / dl_spectral_normalizationLinks
☆13Updated 7 years ago
Alternatives and similar repositories for dl_spectral_normalization
Users that are interested in dl_spectral_normalization are comparing it to the libraries listed below
Sorting:
- Geometric Certifications of Neural Nets☆42Updated 3 years ago
- Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)☆36Updated 5 years ago
- ☆88Updated last year
- [NeurIPS 2018] [JSAIT] PacGAN: The power of two samples in generative adversarial networks☆82Updated 5 years ago
- Scaleable input gradient regularization☆22Updated 6 years ago
- Randomized Smoothing of All Shapes and Sizes (ICML 2020).☆51Updated 5 years ago
- Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]☆31Updated 5 years ago
- Notebooks for reproducing the paper "Computer Vision with a Single (Robust) Classifier"☆129Updated 6 years ago
- Code release for the ICML 2019 paper "Are generative classifiers more robust to adversarial attacks?"☆24Updated 6 years ago
- Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"☆164Updated 5 years ago
- Implementation of Information Dropout☆39Updated 8 years ago
- ☆46Updated 7 years ago
- Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'☆62Updated 7 years ago
- This project is the Torch implementation of our accepted AAAI 2018 paper : orthogonal weight normalization method for solving orthogonali…☆57Updated 6 years ago
- Logit Pairing Methods Can Fool Gradient-Based Attacks [NeurIPS 2018 Workshop on Security in Machine Learning]☆19Updated 7 years ago
- Benchmark for LP-relaxed robustness verification of ReLU-networks☆42Updated 6 years ago
- Analysis of Adversarial Logit Pairing☆60Updated 7 years ago
- Computing various norms/measures on over-parametrized neural networks☆50Updated 7 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
- Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.☆49Updated 6 years ago
- Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs☆101Updated 4 years ago
- ☆21Updated last year
- SGD and Ordered SGD codes for deep learning, SVM, and logistic regression☆36Updated 5 years ago
- Code for "Robustness May Be at Odds with Accuracy"☆91Updated 2 years ago
- Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing the…☆55Updated 3 years ago
- A pytorch implementation of our jacobian regularizer to encourage learning representations more robust to input perturbations.☆129Updated 2 years ago
- Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation [NeurIPS 2017]☆18Updated 7 years ago
- ☆31Updated 5 years ago
- Public code for a paper "Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks."☆35Updated 7 years ago
- A Closer Look at Accuracy vs. Robustness☆88Updated 4 years ago