adebayoj / sanity_checks_saliency
☆109Updated 2 years ago
Related projects ⓘ
Alternatives and complementary repositories for sanity_checks_saliency
- ☆48Updated 4 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠(ICLR 2019)☆125Updated 3 years ago
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆127Updated 3 years ago
- Original dataset release for CIFAR-10H☆82Updated 4 years ago
- reference implementation for "explanations can be manipulated and geometry is to blame"☆35Updated 2 years ago
- Code for the paper "Calibrating Deep Neural Networks using Focal Loss"☆155Updated 10 months ago
- This is the pytorch implementation of the paper - Axiomatic Attribution for Deep Networks.☆181Updated 2 years ago
- code release for the paper "On Completeness-aware Concept-Based Explanations in Deep Neural Networks"☆51Updated 2 years ago
- Detect model's attention☆155Updated 4 years ago
- ☆65Updated 4 years ago
- SmoothGrad implementation in PyTorch☆168Updated 3 years ago
- A pytorch implementation of our jacobian regularizer to encourage learning representations more robust to input perturbations.☆123Updated last year
- This repository provides a PyTorch implementation of "Fooling Neural Network Interpretations via Adversarial Model Manipulation". Our pap…☆22Updated 3 years ago
- Pytorch library for model calibration metrics and visualizations as well as recalibration methods. In progress!☆68Updated 6 months ago
- Combating hidden stratification with GEORGE☆62Updated 3 years ago
- Robust Out-of-distribution Detection in Neural Networks☆72Updated 2 years ago
- Figures & code from the paper "Shortcut Learning in Deep Neural Networks" (Nature Machine Intelligence 2020)☆94Updated 2 years ago
- OD-test: A Less Biased Evaluation of Out-of-Distribution (Outlier) Detectors (PyTorch)☆62Updated last year
- Understanding Deep Networks via Extremal Perturbations and Smooth Masks☆344Updated 4 years ago
- Towards Automatic Concept-based Explanations☆157Updated 6 months ago
- Pytorch Implementation of recent visual attribution methods for model interpretability☆145Updated 4 years ago
- Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty☆129Updated last year
- Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning, ICLR 2020☆235Updated last year
- Calibration of Convolutional Neural Networks☆158Updated last year
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks☆224Updated 5 years ago
- Pytorch implementation of various neural network interpretability methods☆111Updated 2 years ago
- Code for Fong and Vedaldi 2017, "Interpretable Explanations of Black Boxes by Meaningful Perturbation"☆30Updated 5 years ago
- A Closer Look at Accuracy vs. Robustness☆88Updated 3 years ago
- Tools for training explainable models using attribution priors.☆121Updated 3 years ago
- code release for Representer point Selection for Explaining Deep Neural Network in NeurIPS 2018☆67Updated 3 years ago