RuntianZ / adversarial-robustness-unlabeledLinks
Adversarially Robust Generalization Just Requires More Unlabeled Data
☆11Updated 5 years ago
Alternatives and similar repositories for adversarial-robustness-unlabeled
Users that are interested in adversarial-robustness-unlabeled are comparing it to the libraries listed below
Sorting:
- ☆31Updated 4 years ago
- This repository is no longer maintained. Check☆81Updated 5 years ago
- ☆88Updated 11 months ago
- Codebase for "Exploring the Landscape of Spatial Robustness" (ICML'19, https://arxiv.org/abs/1712.02779).☆26Updated 5 years ago
- ☆55Updated 4 years ago
- Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]☆32Updated 5 years ago
- Logit Pairing Methods Can Fool Gradient-Based Attacks [NeurIPS 2018 Workshop on Security in Machine Learning]☆19Updated 6 years ago
- A Closer Look at Accuracy vs. Robustness☆89Updated 4 years ago
- SGD and Ordered SGD codes for deep learning, SVM, and logistic regression☆35Updated 4 years ago
- Code for "Robustness May Be at Odds with Accuracy"☆91Updated 2 years ago
- Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088☆53Updated 6 years ago
- ☆18Updated 5 years ago
- [JMLR] TRADES + random smoothing for certifiable robustness☆14Updated 4 years ago
- Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"☆161Updated 5 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- ☆27Updated 6 years ago
- ICLR 2021, Fair Mixup: Fairness via Interpolation☆56Updated 3 years ago
- Recurrent Back Propagation, Back Propagation Through Optimization, ICML 2018☆42Updated 6 years ago
- Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs☆97Updated 4 years ago
- CVPR'19 experiments with (on-manifold) adversarial examples.☆45Updated 5 years ago
- Official adversarial mixup resynthesis repository☆35Updated 5 years ago
- ICML 2020, Estimating Generalization under Distribution Shifts via Domain-Invariant Representations☆23Updated 5 years ago
- Implementation of Information Dropout☆39Updated 8 years ago
- Public code for a paper "Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks."☆34Updated 6 years ago
- Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks☆66Updated 5 years ago
- Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation [NeurIPS 2017]☆18Updated 7 years ago
- Geometric Certifications of Neural Nets☆42Updated 2 years ago
- Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)☆35Updated 5 years ago
- The code for the paper: https://arxiv.org/pdf/1802.00168.pdf☆17Updated 5 years ago
- Implementation of iterative inference in deep latent variable models☆43Updated 5 years ago