IBM / Contrastive-Explanation-MethodLinks
Codes for reproducing the contrastive explanation in “Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives”
☆54Updated 7 years ago
Alternatives and similar repositories for Contrastive-Explanation-Method
Users that are interested in Contrastive-Explanation-Method are comparing it to the libraries listed below
Sorting:
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆128Updated 4 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆129Updated 4 years ago
- A lightweight implementation of removal-based explanations for ML models.☆59Updated 4 years ago
- Tools for training explainable models using attribution priors.☆125Updated 4 years ago
- Keras implementation for DASP: Deep Approximate Shapley Propagation (ICML 2019)☆62Updated 6 years ago
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆43Updated 3 years ago
- Explaining Image Classifiers by Counterfactual Generation☆28Updated 3 years ago
- Active and Sample-Efficient Model Evaluation☆26Updated 7 months ago
- Model Patching: Closing the Subgroup Performance Gap with Data Augmentation☆42Updated 5 years ago
- Self-Explaining Neural Networks☆43Updated 5 years ago
- Code/figures in Right for the Right Reasons☆57Updated 5 years ago
- To Trust Or Not To Trust A Classifier. A measure of uncertainty for any trained (possibly black-box) classifier which is more effective t…☆177Updated 2 years ago
- ☆125Updated 4 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
- Code for the CVPR 2021 paper: Understanding Failures of Deep Networks via Robust Feature Extraction☆36Updated 3 years ago
- ☆44Updated 5 years ago
- Code for Fong and Vedaldi 2017, "Interpretable Explanations of Black Boxes by Meaningful Perturbation"☆31Updated 6 years ago
- ☆135Updated 6 years ago
- ☆37Updated 2 years ago
- Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]☆50Updated 5 years ago
- code release for the paper "On Completeness-aware Concept-Based Explanations in Deep Neural Networks"☆54Updated 3 years ago
- Code repository for our paper "Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift": https://arxiv.org/abs/1810.119…☆107Updated last year
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆132Updated 5 years ago
- Self-Explaining Neural Networks☆13Updated 2 years ago
- Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.☆51Updated 4 years ago
- A simple algorithm to identify and correct for label shift.☆22Updated 7 years ago
- ☆38Updated 4 years ago
- Model Agnostic Counterfactual Explanations☆88Updated 3 years ago
- This repository contains the full code for the "Towards fairness in machine learning with adversarial networks" blog post.☆119Updated 4 years ago
- Implementation of Adversarial Debiasing in PyTorch to address Gender Bias☆31Updated 5 years ago