Alternatives and similar repositories for kernel_two_sample_test:

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

• xuanqing94 / BayesianDefense
  Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
  ☆62Updated 5 years ago
• duchi-lab / certifiable-distributional-robustness
  Certifying Some Distributional Robustness with Principled Adversarial Training (https://arxiv.org/abs/1710.10571)
  ☆45Updated 7 years ago
• ytsmiling / lmt
  Public code for a paper "Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks."
  ☆34Updated 6 years ago
• lsgos / uncertainty-adversarial-paper
  Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'
  ☆61Updated 6 years ago
• max-andr / provable-robustness-max-linear-regions
  Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]
  ☆32Updated 4 years ago
• amiratag / InterpretationFragility
  Interpretation of Neural Network is Fragile
  ☆36Updated last year
• deepgenerativeclassifier / DeepBayes
  Code release for the ICML 2019 paper "Are generative classifiers more robust to adversarial attacks?"
  ☆23Updated 5 years ago
• fengliu90 / DK-for-TST
  This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).
  ☆49Updated 3 years ago
• btjhjeon / IMM_tensorflow
  Overcoming Catastrophic Forgetting by Incremental Moment Matching (IMM)
  ☆35Updated 7 years ago
• YyzHarry / ME-Net
  [ICML 2019] ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
  ☆54Updated 2 weeks ago
• MadryLab / robust-features-code
  Code for "Robustness May Be at Odds with Accuracy"
  ☆91Updated 2 years ago
• davidstutz / disentangling-robustness-generalization
  CVPR'19 experiments with (on-manifold) adversarial examples.
  ☆44Updated 5 years ago
• marcoancona / DASP
  Keras implementation for DASP: Deep Approximate Shapley Propagation (ICML 2019)
  ☆61Updated 5 years ago
• locuslab / projected_sinkhorn
  ☆87Updated 9 months ago
• yaircarmon / semisup-adv
  Semisupervised learning for adversarial robustness https://arxiv.org/pdf/1905.13736.pdf
  ☆141Updated 5 years ago
• BaoWangMath / LaplacianSmoothing-GradientDescent
  The code for the paper: https://arxiv.org/abs/1806.06317
  ☆24Updated 5 years ago
• dcmoyer / inv-rep
  Code for Invariant Rep. Without Adversaries (NIPS 2018)
  ☆35Updated 5 years ago
• dtak / adversarial-robustness-public
  Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing the…
  ☆55Updated 2 years ago
• dongyp13 / Robust-and-Explainable-Machine-Learning
  Related materials for robust and explainable machine learning
  ☆48Updated 7 years ago
• ShinKyuY / Understanding_Black_box_Predictions_via_Influence_Functions_tutorial_MNIST
  Tiny Tutorial on https://arxiv.org/abs/1703.04730
  ☆13Updated 5 years ago
• uds-lsv / evaluating-logit-pairing-methods
  Logit Pairing Methods Can Fool Gradient-Based Attacks [NeurIPS 2018 Workshop on Security in Machine Learning]
  ☆19Updated 6 years ago
• JHL-HUST / ATDA
  Improving the Generalization of Adversarial Training with Domain Adaptation
  ☆33Updated 6 years ago
• rfeinman / detecting-adversarial-samples
  Code for "Detecting Adversarial Samples from Artifacts" (Feinman et al., 2017)
  ☆109Updated 7 years ago
• max-andr / provably-robust-boosting
  Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]
  ☆50Updated 5 years ago
• MadryLab / adversarial_spatial
  Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.
  ☆49Updated 5 years ago
• BB-UCL / Lasagne
  ☆13Updated 6 years ago
• reiinakano / invariant-risk-minimization
  Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893
  ☆86Updated 5 years ago
• max-andr / cross-lipschitz
  Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation [NeurIPS 2017]
  ☆18Updated 7 years ago
• xingjunm / dimensionality-driven-learning
  Code for paper "Dimensionality-Driven Learning with Noisy Labels" - ICML 2018
  ☆58Updated 10 months ago
• xingjunm / lid_adversarial_subspace_detection
  Code for paper "Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality".
  ☆123Updated 4 years ago