adebayoj / sanity_checks_saliency
☆109Updated 2 years ago
Alternatives and similar repositories for sanity_checks_saliency:
Users that are interested in sanity_checks_saliency are comparing it to the libraries listed below
- ☆51Updated 4 years ago
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆127Updated 4 years ago
- Towards Automatic Concept-based Explanations☆159Updated 11 months ago
- Original dataset release for CIFAR-10H☆82Updated 4 years ago
- This is the pytorch implementation of the paper - Axiomatic Attribution for Deep Networks.☆182Updated 3 years ago
- Detect model's attention☆165Updated 4 years ago
- Code for Fong and Vedaldi 2017, "Interpretable Explanations of Black Boxes by Meaningful Perturbation"☆30Updated 5 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆128Updated 3 years ago
- code release for the paper "On Completeness-aware Concept-Based Explanations in Deep Neural Networks"☆53Updated 3 years ago
- SmoothGrad implementation in PyTorch☆171Updated 3 years ago
- Light version of Network Dissection for Quantifying Interpretability of Networks☆216Updated 5 years ago
- reference implementation for "explanations can be manipulated and geometry is to blame"☆36Updated 2 years ago
- Information Bottlenecks for Attribution☆79Updated 2 years ago
- Quantitative Testing with Concept Activation Vectors in PyTorch☆42Updated 6 years ago
- Figures & code from the paper "Shortcut Learning in Deep Neural Networks" (Nature Machine Intelligence 2020)☆96Updated 2 years ago
- Adversarially Robust Neural Network on MNIST.☆64Updated 3 years ago
- Explaining Image Classifiers by Counterfactual Generation☆28Updated 2 years ago
- Pytorch Implementation of recent visual attribution methods for model interpretability☆145Updated 5 years ago
- PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation☆335Updated 3 years ago
- OD-test: A Less Biased Evaluation of Out-of-Distribution (Outlier) Detectors (PyTorch)☆62Updated last year
- Release of CIFAR-10.1, a new test set for CIFAR-10.☆222Updated 4 years ago
- A pytorch implementation of our jacobian regularizer to encourage learning representations more robust to input perturbations.☆125Updated last year
- Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"☆160Updated 5 years ago
- Python implementation for evaluating explanations presented in "On the (In)fidelity and Sensitivity for Explanations" in NeurIPS 2019 for…☆25Updated 3 years ago
- Understanding Deep Networks via Extremal Perturbations and Smooth Masks☆345Updated 4 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- A PyTorch converter for SimCLR checkpoints☆109Updated 4 years ago
- Interpretation of Neural Network is Fragile☆36Updated 11 months ago
- Robust Out-of-distribution Detection in Neural Networks☆72Updated 2 years ago
- A way to achieve uniform confidence far away from the training data.☆37Updated 3 years ago