adebayoj / sanity_checks_saliencyLinks
☆112Updated 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
Sorting:
- ☆51Updated 4 years ago
- Towards Automatic Concept-based Explanations☆160Updated last year
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆128Updated 4 years ago
- This is the pytorch implementation of the paper - Axiomatic Attribution for Deep Networks.☆185Updated 3 years ago
- Original dataset release for CIFAR-10H☆83Updated 4 years ago
- Full-gradient saliency maps☆212Updated 2 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆129Updated 4 years ago
- code release for the paper "On Completeness-aware Concept-Based Explanations in Deep Neural Networks"☆53Updated 3 years ago
- Code for the paper "Calibrating Deep Neural Networks using Focal Loss"☆160Updated last year
- SmoothGrad implementation in PyTorch☆172Updated 4 years ago
- Detect model's attention☆168Updated 5 years ago
- Calibration of Convolutional Neural Networks☆169Updated 2 years ago
- Code for Fong and Vedaldi 2017, "Interpretable Explanations of Black Boxes by Meaningful Perturbation"☆31Updated 5 years ago
- Figures & code from the paper "Shortcut Learning in Deep Neural Networks" (Nature Machine Intelligence 2020)☆99Updated 3 years ago
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks☆231Updated 6 years ago
- Code for the paper "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks".☆347Updated 6 years ago
- Explaining Image Classifiers by Counterfactual Generation☆28Updated 3 years ago
- This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpreta…☆372Updated 3 years ago
- code release for Representer point Selection for Explaining Deep Neural Network in NeurIPS 2018☆67Updated 3 years ago
- Understanding Deep Networks via Extremal Perturbations and Smooth Masks☆347Updated 5 years ago
- PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation☆338Updated 3 years ago
- A pytorch implementation of our jacobian regularizer to encourage learning representations more robust to input perturbations.☆128Updated last year
- Information Bottlenecks for Attribution☆79Updated 2 years ago
- ☆66Updated 6 years ago
- Combating hidden stratification with GEORGE☆64Updated 4 years ago
- Interpretation of Neural Network is Fragile☆36Updated last year
- OD-test: A Less Biased Evaluation of Out-of-Distribution (Outlier) Detectors (PyTorch)☆62Updated last year
- reference implementation for "explanations can be manipulated and geometry is to blame"☆36Updated 3 years ago
- ☆66Updated 5 years ago
- Pytorch Implementation of recent visual attribution methods for model interpretability☆146Updated 5 years ago