Alternatives and similar repositories for saliency_evaluation

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

• VectorInstitute / gram-ood-detection
  ☆46Updated 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
• YU1ut / Ensemble-of-Leave-out-Classifiers
  Code for "Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers"
  ☆27Updated 3 years ago
• zzzace2000 / FIDO-saliency
  Explaining Image Classifiers by Counterfactual Generation
  ☆28Updated 3 years ago
• MadryLab / DebuggableDeepNetworks
  ☆38Updated 4 years ago
• izmailovpavel / spurious_feature_learning
  ☆45Updated 2 years ago
• amiratag / InterpretationFragility
  Interpretation of Neural Network is Fragile
  ☆36Updated last year
• hendrycks / pre-training
  Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
  ☆100Updated 3 years ago
• AlexMeinke / certified-certain-uncertainty
  A way to achieve uniform confidence far away from the training data.
  ☆38Updated 4 years ago
• jfc43 / robust-ood-detection
  Robust Out-of-distribution Detection in Neural Networks
  ☆73Updated 3 years ago
• alinlab / LfF
  Learning from Failure: Training Debiased Classifier from Biased Classifier (NeurIPS 2020)
  ☆91Updated 4 years 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
• paganpasta / OnMixup
  NeurIPS Reproducbility Challenge 2019
  ☆9Updated 5 years ago
• AmanDaVinci / SENN
  Self-Explaining Neural Networks
  ☆42Updated 5 years ago
• tml-epfl / adv-training-corruptions
  On the effectiveness of adversarial training against common corruptions [UAI 2022]
  ☆30Updated 3 years ago
• dirichletcal / experiments_neurips
  ☆21Updated 2 years ago
• google-research-datasets / bam
  ☆51Updated 4 years ago
• jfc43 / informative-outlier-mining
  We propose a theoretically motivated method, Adversarial Training with informative Outlier Mining (ATOM), which improves the robustness o…
  ☆57Updated 3 years ago
• boschresearch / hierarchical_anomaly_detection
  Implementation of the paper "Understanding anomaly detection with deep invertible networks through hierarchies of distributions and featu…
  ☆42Updated 4 years ago
• princetonvisualai / DomainBiasMitigation
  ☆73Updated 5 years ago
• MadryLab / BREEDS-Benchmarks
  ☆55Updated 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…
  ☆127Updated 4 years ago
• ruthcfong / perturb_explanations
  Code for Fong and Vedaldi 2017, "Interpretable Explanations of Black Boxes by Meaningful Perturbation"
  ☆31Updated 5 years ago
• harshays / inputgradients
  Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)
  ☆13Updated 2 years ago
• nazim1021 / OOD-detection-using-OECC
  Outlier Exposure with Confidence Control for Out-of-Distribution Detection
  ☆69Updated 4 years ago
• JindongGu / Contrastive-LRP
  A pytorch implemention of the Explainable AI work 'Contrastive layerwise relevance propagation (CLRP)'
  ☆17Updated 3 years ago
• cassidylaidlaw / perceptual-advex
  Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".
  ☆55Updated 3 years ago
• pankessel / adv_explanation_ref
  reference implementation for "explanations can be manipulated and geometry is to blame"
  ☆36Updated 2 years ago
• ftramer / MultiRobustness
  Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019
  ☆47Updated 2 years ago
• dmelis / SENN
  Self-Explaining Neural Networks
  ☆13Updated last year