Alternatives and similar repositories for adv_explanation_ref:

Users that are interested in adv_explanation_ref 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
• 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
• charan223 / FairDeepLearning
  ☆37Updated 2 years ago
• princetonvisualai / DomainBiasMitigation
  ☆73Updated 4 years ago
• google-research-datasets / bam
  ☆51Updated 4 years ago
• F-Salehi / CURE_robustness
  Implemented CURE algorithm from robustness via curvature regularization and vice versa
  ☆31Updated 2 years ago
• harshays / simplicitybiaspitfalls
  The Pitfalls of Simplicity Bias in Neural Networks [NeurIPS 2020] (http://arxiv.org/abs/2006.07710v2)
  ☆39Updated last year
• MadryLab / BREEDS-Benchmarks
  ☆54Updated 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
• zhangrh93 / InvertibleCE
  Invertible Concept-based Explanation (ICE)
  ☆18Updated 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…
  ☆127Updated 4 years ago
• bethgelab / AnalysisBySynthesis
  Adversarially Robust Neural Network on MNIST.
  ☆64Updated 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
• ddkang / advex-uar
  Code for "Testing Robustness Against Unforeseen Adversaries"
  ☆81Updated 8 months ago
• amiratag / InterpretationFragility
  Interpretation of Neural Network is Fragile
  ☆36Updated 11 months ago
• adebayoj / sanity_checks_saliency
  ☆109Updated 2 years ago
• AdityaGolatkar / SelectiveForgetting
  ☆56Updated 4 years ago
• jcpeterson / cifar-10h
  Original dataset release for CIFAR-10H
  ☆82Updated 4 years ago
• wronnyhuang / gen-viz
  Code for the paper "Understanding Generalization through Visualizations"
  ☆60Updated 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
• tml-epfl / understanding-fast-adv-training
  Understanding and Improving Fast Adversarial Training [NeurIPS 2020]
  ☆96Updated 3 years ago
• tml-epfl / adv-training-corruptions
  On the effectiveness of adversarial training against common corruptions [UAI 2022]
  ☆30Updated 2 years ago
• anniesch / jtt
  Code for "Just Train Twice: Improving Group Robustness without Training Group Information"
  ☆71Updated 10 months ago
• AkhilanB / Proper-Interpretability
  Codes for reproducing the experimental results in "Proper Network Interpretability Helps Adversarial Robustness in Classification", publi…
  ☆13Updated 4 years ago
• LTS4 / neural-anisotropy-directions
  Source code for "Neural Anisotropy Directions"
  ☆15Updated 4 years ago
• AlexBinder / LRP_Pytorch_Resnets_Densenet
  implements some LRP rules to get explanations for Resnets and Densenet-121, including batchnorm-Conv canonization and tensorbiased layers…
  ☆25Updated last year
• harshays / inputgradients
  Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)
  ☆13Updated 2 years ago
• RulinShao / on-the-adversarial-robustness-of-visual-transformer
  Code for the paper "On the Adversarial Robustness of Visual Transformers"
  ☆56Updated 3 years ago
• yuji-roh / fairbatch
  FairBatch: Batch Selection for Model Fairness (ICLR 2021)
  ☆19Updated last year
• singlasahil14 / salient_imagenet
  Code for the ICLR 2022 paper. Salient Imagenet: How to discover spurious features in deep learning?
  ☆40Updated 2 years ago