Alternatives and similar repositories for FIDO-saliency

Users that are interested in FIDO-saliency are comparing it to the libraries listed below

• AlexMeinke / certified-certain-uncertainty
  A way to achieve uniform confidence far away from the training data.
  ☆38Updated 4 years ago
• dmelis / SENN
  Self-Explaining Neural Networks
  ☆13Updated last year
• capybaralet / REx_code_release
  ☆63Updated 4 years ago
• facebookresearch / BalancingGroups
  Simple data balancing baselines for worst-group-accuracy benchmarks.
  ☆42Updated last year
• google-research-datasets / bam
  ☆51Updated 4 years ago
• ruthcfong / perturb_explanations
  Code for Fong and Vedaldi 2017, "Interpretable Explanations of Black Boxes by Meaningful Perturbation"
  ☆31Updated 5 years ago
• bneyshabur / generalization-bounds
  Computing various measures and generalization bounds on convolutional and fully connected networks
  ☆35Updated 6 years ago
• MadryLab / BREEDS-Benchmarks
  ☆55Updated 4 years ago
• davidstutz / disentangling-robustness-generalization
  CVPR'19 experiments with (on-manifold) adversarial examples.
  ☆45Updated 5 years ago
• bethgelab / InDomainGeneralizationBenchmark
  ☆34Updated 3 years ago
• MadryLab / DebuggableDeepNetworks
  ☆38Updated 3 years ago
• reiinakano / invariant-risk-minimization
  Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893
  ☆88Updated 5 years ago
• dcmoyer / inv-rep
  Code for Invariant Rep. Without Adversaries (NIPS 2018)
  ☆35Updated 5 years ago
• chingyaoc / fair-mixup
  ICLR 2021, Fair Mixup: Fairness via Interpolation
  ☆56Updated 3 years ago
• haoliuhl / hybrid-discriminative-generative
  Hybrid Discriminative-Generative Training via Contrastive Learning
  ☆75Updated 2 years ago
• bneyshabur / over-parametrization
  Computing various norms/measures on over-parametrized neural networks
  ☆49Updated 6 years ago
• facebookresearch / InvarianceUnitTests
  Toy datasets to evaluate algorithms for domain generalization and invariance learning.
  ☆42Updated 3 years ago
• hendrycks / pre-training
  Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
  ☆100Updated 3 years ago
• jhjacobsen / fully-invertible-revnet
  ☆31Updated 4 years ago
• Faruk-Ahmed / predictive_group_invariance
  ☆34Updated 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
• HaohanWang / HEX
  Example implementation for the paper: (ICLR Oral) Learning Robust Representations by Projecting Superficial Statistics Out
  ☆27Updated 4 years ago
• conormdurkan / neural-statistician
  PyTorch Implementation of Neural Statistician
  ☆60Updated 3 years ago
• IirisSundin / active-learning-for-decision-making
  This repository contains the code used in a publication 'Active Learning for Decision-Making from Imbalanced Observational Data', Iiris S…
  ☆11Updated 6 years ago
• SeojinBang / VIBI
  Explaining a black-box using Deep Variational Information Bottleneck Approach
  ☆46Updated 2 years ago
• IBM / OoD
  Repository for theory and methods for Out-of-Distribution (OoD) generalization
  ☆63Updated 3 years ago
• mpezeshki / Gradient_Starvation
  Gradient Starvation: A Learning Proclivity in Neural Networks
  ☆61Updated 4 years ago
• amiratag / InterpretationFragility
  Interpretation of Neural Network is Fragile
  ☆36Updated last year
• fengliu90 / DK-for-TST
  This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).
  ☆49Updated 3 years ago
• harshays / inputgradients
  Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)
  ☆13Updated 2 years ago