Alternatives and similar repositories for Contrastive-Explanation-Method

Users that are interested in Contrastive-Explanation-Method are comparing it to the libraries listed below

• csinva / hierarchical-dnn-interpretations
  Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
  ☆129Updated 4 years ago
• laura-rieger / deep-explanation-penalization
  Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…
  ☆128Updated 4 years ago
• iancovert / removal-explanations
  A lightweight implementation of removal-based explanations for ML models.
  ☆59Updated 4 years ago
• suinleelab / attributionpriors
  Tools for training explainable models using attribution priors.
  ☆124Updated 4 years ago
• dtak / rrr
  Code/figures in Right for the Right Reasons
  ☆57Updated 4 years ago
• Jianbo-Lab / L2X
  ☆124Updated 4 years ago
• marcoancona / DASP
  Keras implementation for DASP: Deep Approximate Shapley Propagation (ICML 2019)
  ☆61Updated 6 years ago
• chihkuanyeh / saliency_evaluation
  Python implementation for evaluating explanations presented in "On the (In)fidelity and Sensitivity for Explanations" in NeurIPS 2019 for…
  ☆25Updated 3 years ago
• datamllab / xdeep
  ☆44Updated 5 years ago
• HazyResearch / mandoline
  (ICML 2021) Mandoline: Model Evaluation under Distribution Shift
  ☆30Updated 4 years ago
• BeenKim / MMD-critic
  ☆134Updated 6 years ago
• AmanDaVinci / SENN
  Self-Explaining Neural Networks
  ☆43Updated 5 years ago
• zzzace2000 / FIDO-saliency
  Explaining Image Classifiers by Counterfactual Generation
  ☆28Updated 3 years ago
• charmlab / mace
  Model Agnostic Counterfactual Explanations
  ☆88Updated 3 years ago
• ruthcfong / perturb_explanations
  Code for Fong and Vedaldi 2017, "Interpretable Explanations of Black Boxes by Meaningful Perturbation"
  ☆31Updated 6 years ago
• HazyResearch / model-patching
  Model Patching: Closing the Subgroup Performance Gap with Data Augmentation
  ☆42Updated 4 years ago
• ykwon0407 / beta_shapley
  Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)
  ☆41Updated 2 years ago
• OscarcarLi / PrototypeDL
  Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AA…
  ☆76Updated 7 years ago
• google / TrustScore
  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
• Piyushi-0 / ACE
  Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.
  ☆51Updated 3 years ago
• amiratag / ACE
  Towards Automatic Concept-based Explanations
  ☆161Updated last year
• sfme / RVAE_MixedTypes
  Repository for code release of paper "Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data" (AISTATS 2020)
  ☆50Updated 5 years ago
• dmitrykazhdan / concept-based-xai
  Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
  ☆54Updated 3 years ago
• GDPlumb / MAPLE
  Supervised Local Modeling for Interpretability
  ☆29Updated 7 years ago
• HazyResearch / hidden-stratification
  Combating hidden stratification with GEORGE
  ☆64Updated 4 years ago
• jlko / active-testing
  Active and Sample-Efficient Model Evaluation
  ☆25Updated 5 months ago
• d909b / cxplain
  Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
  ☆132Updated 5 years ago
• pietrobarbiero / logic_explained_networks
  Logic Explained Networks is a python repository implementing explainable-by-design deep learning models.
  ☆51Updated 2 years ago
• facebookresearch / fisher_information_loss
  This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"
  ☆50Updated 4 years ago
• chihkuanyeh / concept_exp
  code release for the paper "On Completeness-aware Concept-Based Explanations in Deep Neural Networks"
  ☆53Updated 3 years ago