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:
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆129Updated 4 years ago
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆128Updated 4 years ago
- A lightweight implementation of removal-based explanations for ML models.☆59Updated 4 years ago
- Explaining Image Classifiers by Counterfactual Generation☆28Updated 3 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
- Tools for training explainable models using attribution priors.☆125Updated 4 years ago
- Code/figures in Right for the Right Reasons☆57Updated 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
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆43Updated 3 years ago
- ☆135Updated 6 years ago
- ☆125Updated 4 years ago
- Active and Sample-Efficient Model Evaluation☆26Updated 8 months ago
- Code for the CVPR 2021 paper: Understanding Failures of Deep Networks via Robust Feature Extraction☆36Updated 3 years ago
- Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AA…☆77Updated 8 years ago
- Code for our ICML '19 oral paper: Neural Network Attributions: A Causal Perspective.☆51Updated 4 years ago
- Self-Explaining Neural Networks☆44Updated 6 years ago
- This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"☆49Updated 4 years ago
- Keras implementation for DASP: Deep Approximate Shapley Propagation (ICML 2019)☆62Updated 6 years ago
- ☆113Updated 3 years ago
- code release for the paper "On Completeness-aware Concept-Based Explanations in Deep Neural Networks"☆54Updated 3 years ago
- Self-Explaining Neural Networks☆13Updated 2 years ago
- Code for "Testing Robustness Against Unforeseen Adversaries"☆80Updated last year
- (ICML 2021) Mandoline: Model Evaluation under Distribution Shift☆30Updated 4 years ago
- Code release for the ICML 2019 paper "Are generative classifiers more robust to adversarial attacks?"☆24Updated 6 years ago
- Code for Fong and Vedaldi 2017, "Interpretable Explanations of Black Boxes by Meaningful Perturbation"☆32Updated 6 years ago
- This is a benchmark to evaluate machine learning local explanaitons quality generated from any explainer for text and image data☆30Updated 4 years ago
- ☆51Updated 5 years ago
- Model Agnostic Counterfactual Explanations☆88Updated 3 years ago
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆132Updated 5 years ago
- Interpretation of Neural Network is Fragile☆36Updated last year