Alternatives and similar repositories for saliency_evaluation

Users that are interested in saliency_evaluation 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"
  ☆54Updated 3 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
• rmrisforbidden / Fooling_Neural_Network-Interpretations
  This repository provides a PyTorch implementation of "Fooling Neural Network Interpretations via Adversarial Model Manipulation". Our pap…
  ☆23Updated 4 years ago
• microsoft / robustdg
  Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.
  ☆175Updated 2 years ago
• AlexMeinke / certified-certain-uncertainty
  A way to achieve uniform confidence far away from the training data.
  ☆38Updated 4 years ago
• zzzace2000 / FIDO-saliency
  Explaining Image Classifiers by Counterfactual Generation
  ☆28Updated 3 years ago
• VectorInstitute / gram-ood-detection
  ☆46Updated 4 years ago
• adebayoj / sanity_checks_saliency
  ☆112Updated 3 years ago
• pankessel / adv_explanation_ref
  reference implementation for "explanations can be manipulated and geometry is to blame"
  ☆37Updated 3 years ago
• google-research-datasets / bam
  ☆51Updated 5 years ago
• MadryLab / DebuggableDeepNetworks
  ☆38Updated 4 years ago
• jfc43 / robust-ood-detection
  Robust Out-of-distribution Detection in Neural Networks
  ☆73Updated 3 years ago
• zhang64-llnl / Mix-n-Match-Calibration
  ☆38Updated 5 years ago
• amiratag / InterpretationFragility
  Interpretation of Neural Network is Fragile
  ☆36Updated last year
• torrvision / focal_calibration
  Code for the paper "Calibrating Deep Neural Networks using Focal Loss"
  ☆161Updated last year
• nazim1021 / OOD-detection-using-OECC
  Outlier Exposure with Confidence Control for Out-of-Distribution Detection
  ☆71Updated 4 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
• fengliu90 / DK-for-TST
  This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).
  ☆51Updated 4 years ago
• chihhuiho / CLAE
  Implementation of Contrastive Learning with Adversarial Examples
  ☆29Updated 4 years ago
• hendrycks / pre-training
  Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
  ☆100Updated 3 years ago
• KaosEngineer / PriorNetworks
  ☆66Updated 5 years ago
• reiinakano / invariant-risk-minimization
  Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893
  ☆91Updated 5 years ago
• davidstutz / disentangling-robustness-generalization
  CVPR'19 experiments with (on-manifold) adversarial examples.
  ☆45Updated 5 years ago
• rgeirhos / shortcut-perspective
  Figures & code from the paper "Shortcut Learning in Deep Neural Networks" (Nature Machine Intelligence 2020)
  ☆101Updated 3 years ago
• rakhimovv / tcav
  Quantitative Testing with Concept Activation Vectors in PyTorch
  ☆43Updated 6 years ago
• IBM / Contrastive-Explanation-Method
  Codes for reproducing the contrastive explanation in “Explanations based on the Missing: Towards Contrastive Explanations with Pertinent…
  ☆54Updated 7 years ago
• csinva / hierarchical-dnn-interpretations
  Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
  ☆129Updated 4 years ago
• dirichletcal / experiments_neurips
  ☆21Updated 2 years ago
• ruthcfong / perturb_explanations
  Code for Fong and Vedaldi 2017, "Interpretable Explanations of Black Boxes by Meaningful Perturbation"
  ☆31Updated 6 years ago
• amiratag / ACE
  Towards Automatic Concept-based Explanations
  ☆161Updated last year