Alternatives and similar repositories for xdeep:

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

• chenhongge / RobustTrees
  [ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples
  ☆67Updated 2 years ago
• jlko / active-testing
  Active and Sample-Efficient Model Evaluation
  ☆24Updated 3 years ago
• max-andr / provably-robust-boosting
  Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]
  ☆51Updated 4 years ago
• 1202kbs / Robustness-and-Interpretability
  Official repository for "Bridging Adversarial Robustness and Gradient Interpretability".
  ☆30Updated 5 years ago
• amiratag / InterpretationFragility
  Interpretation of Neural Network is Fragile
  ☆36Updated 9 months ago
• chihkuanyeh / saliency_evaluation
  Python implementation for evaluating explanations presented in "On the (In)fidelity and Sensitivity for Explanations" in NeurIPS 2019 for…
  ☆25Updated 2 years ago
• amiratag / DistributionalShapley
  Distributional Shapley: A Distributional Framework for Data Valuation
  ☆30Updated 9 months ago
• AI-secure / KNN-PVLDB
  Official Repo for "Efficient task-specific data valuation for nearest neighbor algorithms"
  ☆26Updated 5 years ago
• locuslab / perturbation_learning
  Learning perturbation sets for robust machine learning
  ☆64Updated 3 years ago
• mndu / guided-feature-inversion
  PyTorch code for KDD 18 paper: Towards Explanation of DNN-based Prediction with Guided Feature Inversion
  ☆21Updated 6 years ago
• yaodongyu / Rethink-BiasVariance-Tradeoff
  Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
  ☆49Updated 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
• marcoancona / DASP
  Keras implementation for DASP: Deep Approximate Shapley Propagation (ICML 2019)
  ☆61Updated 5 years ago
• datamllab / autokaggle
  Automated Machine Learning (AutoML) for Kaggle Competition
  ☆32Updated last year
• davidstutz / disentangling-robustness-generalization
  CVPR'19 experiments with (on-manifold) adversarial examples.
  ☆44Updated 4 years ago
• sayakpaul / par-cvpr-21
  Contains notebooks for the PAR tutorial at CVPR 2021.
  ☆36Updated 3 years ago
• snap-stanford / crust
  [NeurIPS 2020] Coresets for Robust Training of Neural Networks against Noisy Labels
  ☆32Updated 3 years ago
• ykwon0407 / beta_shapley
  Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)
  ☆40Updated 2 years ago
• senwu / dauphin
  An uncertainty-based random sampling algorithm for data augmentation
  ☆30Updated 4 years ago
• nesl / Explainability-Study
  How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods
  ☆23Updated 4 years ago
• Jianbo-Lab / L2X
  ☆124Updated 3 years ago
• csinva / hierarchical-dnn-interpretations
  Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
  ☆128Updated 3 years ago
• HazyResearch / mandoline
  (ICML 2021) Mandoline: Model Evaluation under Distribution Shift
  ☆31Updated 3 years ago
• MadryLab / modeldiff
  ModelDiff: A Framework for Comparing Learning Algorithms
  ☆55Updated last year
• kohpangwei / group-influence-release
  ☆50Updated last year
• ajsanjoaquin / Shapley_Valuation
  PyTorch reimplementation of computing Shapley values via Truncated Monte Carlo sampling from "What is your data worth? Equitable Valuatio…
  ☆25Updated 3 years ago
• ninghaohello / Interpretation2Adversary
  Adversarial learning by utilizing model interpretation
  ☆10Updated 6 years ago
• xuanqing94 / BayesianDefense
  Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
  ☆63Updated 5 years ago
• facebookresearch / fisher_information_loss
  This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"
  ☆49Updated 3 years ago
• hongyanz / TRADES-smoothing
  [JMLR] TRADES + random smoothing for certifiable robustness
  ☆14Updated 4 years ago