Alternatives and similar repositories for rcav

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

• dmelis / SENN
  Self-Explaining Neural Networks
  ☆13Updated last year
• asmadotgh / dissect
  DISSECT: Disentangled Simultaneous Explanations via Concept Traversals
  ☆11Updated last year
• MadryLab / DebuggableDeepNetworks
  ☆38Updated 3 years ago
• LTS4 / hold-me-tight
  Source code of "Hold me tight! Influence of discriminative features on deep network boundaries"
  ☆22Updated 3 years ago
• singlasahil14 / barlow
  Code for the CVPR 2021 paper: Understanding Failures of Deep Networks via Robust Feature Extraction
  ☆36Updated 3 years ago
• davidstutz / disentangling-robustness-generalization
  CVPR'19 experiments with (on-manifold) adversarial examples.
  ☆45Updated 5 years ago
• zzzace2000 / FIDO-saliency
  Explaining Image Classifiers by Counterfactual Generation
  ☆28Updated 3 years ago
• dmitrykazhdan / CME
  CME: Concept-based Model Extraction
  ☆11Updated 4 years ago
• MadryLab / ImageNetMultiLabel
  Fine-grained ImageNet annotations
  ☆29Updated 5 years ago
• eminorhan / ood-benchmarks
  Out-of-distribution generalization benchmarks for image recognition models
  ☆14Updated 5 years ago
• HaohanWang / PAR_experiments
  Learning Robust Global Representations by Penalizing Local Predictive Power (NeurIPS 2019))
  ☆18Updated 2 years ago
• JonathanCrabbe / CARs
  This repository contains the implementation of Concept Activation Regions, a new framework to explain deep neural networks with human con…
  ☆11Updated 2 years ago
• princetonvisualai / OverlookedFactors
  Overlooked Factors in Concept-based Explanations: Dataset Choice, Concept Learnability, and Human Capability (CVPR 2023)
  ☆9Updated 2 years ago
• UMBCvision / fooling_network_interpretation
  Official PyTorch implementation for our ICCV 2019 paper - Fooling Network Interpretation in Image Classification
  ☆24Updated 5 years ago
• chingyaoc / fair-mixup
  ICLR 2021, Fair Mixup: Fairness via Interpolation
  ☆56Updated 3 years ago
• liuchen11 / AdversaryLossLandscape
  On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them [NeurIPS 2020]
  ☆36Updated 3 years ago
• zhang64-llnl / Mix-n-Match-Calibration
  ☆35Updated 4 years ago
• google-research-datasets / bam
  ☆51Updated 4 years ago
• alceubissoto / debiasing-skin
  ☆17Updated 4 years ago
• tml-epfl / adv-training-corruptions
  On the effectiveness of adversarial training against common corruptions [UAI 2022]
  ☆30Updated 3 years ago
• parthnatekar / pgdl
  Winning Solution of the NeurIPS 2020 Competition on Predicting Generalization in Deep Learning
  ☆40Updated 4 years ago
• harshays / inputgradients
  Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)
  ☆13Updated 2 years ago
• ahujak / IB-IRM
  ☆30Updated 3 years ago
• amodas / PRIME-augmentations
  PRIME: A Few Primitives Can Boost Robustness to Common Corruptions
  ☆42Updated 2 years ago
• EvZissel / Residual-Flow
  An implementation of the Residual Flow algorithm for out-of-distribution detection.
  ☆31Updated 3 years ago
• henrygouk / mars-finetuning
  ☆19Updated 4 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
• Newbeeer / TRM
  Learning Representations that Support Robust Transfer of Predictors
  ☆20Updated 3 years ago
• luoyan407 / congruency
  [TPAMI 2019] The implementation for "Direction Concentration Learning: Enhancing Congruency in Machine Learning"
  ☆23Updated 5 years ago
• amiratag / neuronshapley
  Code for "Neuron Shapley: Discovering the Responsible Neurons"
  ☆26Updated last year