facebookresearch / fisher_information_loss
This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"
☆49Updated 3 years ago
Related projects ⓘ
Alternatives and complementary repositories for fisher_information_loss
- Code for the CVPR 2021 paper: Understanding Failures of Deep Networks via Robust Feature Extraction☆35Updated 2 years ago
- ☆29Updated 5 years ago
- ☆37Updated 3 years ago
- Source code of "Hold me tight! Influence of discriminative features on deep network boundaries"☆22Updated 2 years ago
- ☆55Updated 4 years ago
- A Closer Look at Accuracy vs. Robustness☆88Updated 3 years ago
- ☆25Updated 4 years ago
- Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]☆31Updated 4 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆99Updated 2 years ago
- Code for the paper "Understanding Generalization through Visualizations"☆60Updated 3 years ago
- Gradient Starvation: A Learning Proclivity in Neural Networks☆60Updated 3 years ago
- CVPR'19 experiments with (on-manifold) adversarial examples.☆44Updated 4 years ago
- Geometric Certifications of Neural Nets☆41Updated 2 years ago
- ☆20Updated 3 months ago
- ☆87Updated 3 months ago
- Code for the Paper 'On the Connection Between Adversarial Robustness and Saliency Map Interpretability' by C. Etmann, S. Lunz, P. Maass, …☆16Updated 5 years ago
- Scaleable input gradient regularization☆22Updated 5 years ago
- PyTorch Implementation of CVPR'19 (oral) - Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach☆27Updated 5 years ago
- [ICLR 2020] ”Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference“☆24Updated 2 years ago
- Smooth Adversarial Training☆67Updated 4 years ago
- Understanding and Improving Fast Adversarial Training [NeurIPS 2020]☆95Updated 3 years ago
- Rethinking Bias-Variance Trade-off for Generalization of Neural Networks☆49Updated 3 years ago
- Implementation of Confidence-Calibrated Adversarial Training (CCAT).☆45Updated 4 years ago
- Learning perturbation sets for robust machine learning☆64Updated 3 years ago
- ☆61Updated 3 years ago
- Tilted Empirical Risk Minimization (ICLR '21)☆59Updated last year
- Official PyTorch implementation of “Flexible Dataset Distillation: Learn Labels Instead of Images”☆41Updated 4 years ago
- [JMLR] TRADES + random smoothing for certifiable robustness☆14Updated 4 years ago
- ☆35Updated last year
- Official repository for "Bridging Adversarial Robustness and Gradient Interpretability".☆30Updated 5 years ago