da2so / Interpretable-Explanations-of-Black-Boxes-by-Meaningful-PerturbationLinks
Interpretable Explanations of Black Boxes by Meaningful Perturbation Pytorch
☆12Updated 9 months ago
Alternatives and similar repositories for Interpretable-Explanations-of-Black-Boxes-by-Meaningful-Perturbation
Users that are interested in Interpretable-Explanations-of-Black-Boxes-by-Meaningful-Perturbation are comparing it to the libraries listed below
Sorting:
- Counterfactual Explanation Based on Gradual Construction for Deep Networks Pytorch☆11Updated 4 years ago
- Source code of "Hold me tight! Influence of discriminative features on deep network boundaries"☆22Updated 3 years ago
- Fine-grained ImageNet annotations☆29Updated 5 years ago
- A general method for training cost-sensitive robust classifier☆22Updated 6 years ago
- ZSKD with PyTorch☆31Updated last year
- Contains notebooks for the PAR tutorial at CVPR 2021.☆36Updated 3 years ago
- CVPR'19 experiments with (on-manifold) adversarial examples.☆45Updated 5 years ago
- Pytorch implementation for "The Surprising Positive Knowledge Transfer in Continual 3D Object Shape Reconstruction"☆33Updated 2 years ago
- Official repository for "Bridging Adversarial Robustness and Gradient Interpretability".☆30Updated 6 years ago
- Official PyTorch implementation for our ICCV 2019 paper - Fooling Network Interpretation in Image Classification☆24Updated 5 years ago
- PyTorch code for KDD 18 paper: Towards Explanation of DNN-based Prediction with Guided Feature Inversion☆21Updated 6 years ago
- PyTorch reimplementation of computing Shapley values via Truncated Monte Carlo sampling from "What is your data worth? Equitable Valuatio…☆27Updated 3 years ago
- Code for the CVPR 2021 paper: Understanding Failures of Deep Networks via Robust Feature Extraction☆36Updated 3 years ago
- Official Repo for "Efficient task-specific data valuation for nearest neighbor algorithms"☆26Updated 5 years ago
- Code for CVPR2021 paper: MOOD: Multi-level Out-of-distribution Detection☆38Updated last year
- Python implementation for evaluating explanations presented in "On the (In)fidelity and Sensitivity for Explanations" in NeurIPS 2019 for…☆25Updated 3 years ago
- ICML'20: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust☆15Updated 4 years ago
- Code for Overinterpretation paper☆19Updated last year
- Official PyTorch implementation of “Flexible Dataset Distillation: Learn Labels Instead of Images”☆42Updated 4 years ago
- Code for Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks☆30Updated 7 years ago
- Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2…☆23Updated 4 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
- Official repository for Reliable Label Bootstrapping☆19Updated 2 years ago
- Tensorflow implementation of Meta Adversarial Training for Adversarial Patch Attacks on Tiny ImageNet.☆25Updated 4 years ago
- ☆18Updated 3 years ago
- Gradient Starvation: A Learning Proclivity in Neural Networks☆61Updated 4 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- Distributional Shapley: A Distributional Framework for Data Valuation☆30Updated last year
- ☆46Updated 4 years ago
- Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network☆62Updated 5 years ago