Alternatives and similar repositories for tcav:

Users that are interested in tcav are comparing it to the libraries listed below

• chihkuanyeh / concept_exp
  code release for the paper "On Completeness-aware Concept-Based Explanations in Deep Neural Networks"
  ☆53Updated 3 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
• mlomnitz / tcav_pytorch
  Pytorch implementation of Google TCAV
  ☆10Updated 6 years ago
• adebayoj / sanity_checks_saliency
  ☆109Updated 2 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…
  ☆127Updated 4 years ago
• BioroboticsLab / IBA
  Information Bottlenecks for Attribution
  ☆79Updated 2 years ago
• AlexMeinke / certified-certain-uncertainty
  A way to achieve uniform confidence far away from the training data.
  ☆38Updated 4 years ago
• KaosEngineer / PriorNetworks
  ☆66Updated 5 years ago
• zzzace2000 / FIDO-saliency
  Explaining Image Classifiers by Counterfactual Generation
  ☆28Updated 3 years ago
• ashafaei / OD-test
  OD-test: A Less Biased Evaluation of Out-of-Distribution (Outlier) Detectors (PyTorch)
  ☆62Updated last year
• google-research-datasets / bam
  ☆51Updated 4 years ago
• IntelLabs / AVUC
  Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.
  ☆55Updated 2 years ago
• VectorInstitute / gram-ood-detection
  ☆46Updated 4 years ago
• amiratag / ACE
  Towards Automatic Concept-based Explanations
  ☆159Updated 11 months ago
• CSAILVision / IBD
  IBD: Interpretable Basis Decomposition for Visual Explanation
  ☆52Updated 6 years ago
• alexrame / fishr
  Official PyTorch implementation of the Fishr regularization for out-of-distribution generalization
  ☆86Updated 2 years ago
• microsoft / robustdg
  Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.
  ☆176Updated last year
• QingyuZhao / BR-Net
  ☆46Updated 4 years ago
• amiratag / InterpretationFragility
  Interpretation of Neural Network is Fragile
  ☆36Updated 11 months ago
• rmrisforbidden / Fooling_Neural_Network-Interpretations
  This repository provides a PyTorch implementation of "Fooling Neural Network Interpretations via Adversarial Model Manipulation". Our pap…
  ☆22Updated 4 years ago
• hendrycks / pre-training
  Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
  ☆100Updated 3 years ago
• MadryLab / backgrounds_challenge
  ☆140Updated 4 years ago
• csinva / hierarchical-dnn-interpretations
  Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
  ☆128Updated 3 years ago
• paganpasta / OnMixup
  NeurIPS Reproducbility Challenge 2019
  ☆9Updated 5 years ago
• belaalb / G2DM
  Generalizing to unseen domains via distribution matching
  ☆72Updated 4 years ago
• omegafragger / DDU
  Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty
  ☆139Updated last year
• princetonvisualai / DomainBiasMitigation
  ☆73Updated 5 years ago
• jcpeterson / cifar-10h
  Original dataset release for CIFAR-10H
  ☆82Updated 4 years ago
• zhang64-llnl / Mix-n-Match-Calibration
  ☆34Updated 4 years ago
• wiseodd / last_layer_laplace
  Last-layer Laplace approximation code examples
  ☆83Updated 3 years ago