da2so / Interpretable-Explanations-of-Black-Boxes-by-Meaningful-Perturbation
Interpretable Explanations of Black Boxes by Meaningful Perturbation Pytorch
☆12Updated 8 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
- PyTorch code for KDD 18 paper: Towards Explanation of DNN-based Prediction with Guided Feature Inversion☆21Updated 6 years ago
- Pytorch implementation for "The Surprising Positive Knowledge Transfer in Continual 3D Object Shape Reconstruction"☆33Updated 2 years ago
- Official PyTorch implementation of “Flexible Dataset Distillation: Learn Labels Instead of Images”☆42Updated 4 years ago
- Implementation of the paper Identifying Mislabeled Data using the Area Under the Margin Ranking: https://arxiv.org/pdf/2001.10528v2.pdf☆21Updated 5 years ago
- CVPR'19 experiments with (on-manifold) adversarial examples.☆45Updated 5 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- ICML'20: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust☆15Updated 4 years ago
- Distributional Shapley: A Distributional Framework for Data Valuation☆30Updated last year
- Label shift experiments☆17Updated 4 years ago
- Source code of "Hold me tight! Influence of discriminative features on deep network boundaries"☆22Updated 3 years ago
- ZSKD with PyTorch☆30Updated last year
- Code for "Interpretable Image Recognition with Hierarchical Prototypes"☆18Updated 5 years ago
- [NeurIPS 2020] Coresets for Robust Training of Neural Networks against Noisy Labels☆34Updated 4 years ago
- AAAI 2021: Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels☆23Updated 3 years ago
- Fine-grained ImageNet annotations☆29Updated 4 years ago
- Code for the paper "Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets", ICCV 2019☆27Updated 5 years ago
- Interpretation of Neural Network is Fragile☆36Updated last year
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network☆62Updated 5 years ago
- Implementation "Adapting Auxiliary Losses Using Gradient Similarity" article☆32Updated 6 years ago
- Code for Fong and Vedaldi 2017, "Interpretable Explanations of Black Boxes by Meaningful Perturbation"☆31Updated 5 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
- ☆18Updated 3 years ago
- Sinkhorn Label Allocation is a label assignment method for semi-supervised self-training algorithms. The SLA algorithm is described in fu…☆53Updated 3 years ago
- Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2…☆22Updated 4 years ago
- Implementation for What it Thinks is Important is Important: Robustness Transfers through Input Gradients (CVPR 2020 Oral)☆16Updated 2 years ago
- Implementation of the paper "Understanding anomaly detection with deep invertible networks through hierarchies of distributions and featu…☆41Updated 4 years ago
- ☆19Updated 4 years ago
- Official PyTorch implementation for our ICCV 2019 paper - Fooling Network Interpretation in Image Classification☆24Updated 5 years ago