Alternatives and similar repositories for attack_vae

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

• facebookresearch / BalancingGroups
  Simple data balancing baselines for worst-group-accuracy benchmarks.
  ☆42Updated last year
• GT-RIPL / Geometric-Sensitivity-Decomposition
  ☆18Updated 3 years ago
• iamalexkorotin / Wasserstein2Barycenters
  PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)
  ☆23Updated 3 years ago
• RuntianZ / doro
  Distributional and Outlier Robust Optimization (ICML 2021)
  ☆27Updated 4 years ago
• locuslab / orthogonal-convolutions
  Implementations of orthogonal and semi-orthogonal convolutions in the Fourier domain with applications to adversarial robustness
  ☆46Updated 4 years ago
• mfederici / dsit
  Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"
  ☆25Updated 3 years ago
• ahujak / IB-IRM
  ☆31Updated 3 years ago
• preetum / cifar5m
  CIFAR-5m dataset
  ☆39Updated 4 years ago
• litian96 / TERM
  Tilted Empirical Risk Minimization (ICLR '21)
  ☆59Updated last year
• MadryLab / DebuggableDeepNetworks
  ☆38Updated 4 years ago
• chingyaoc / fair-mixup
  ICLR 2021, Fair Mixup: Fairness via Interpolation
  ☆56Updated 3 years ago
• AnonymousNIPS2019 / DeepnetHessian
  The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size
  ☆17Updated 6 years ago
• sndnyang / JEMPP
  PyTorch implementation of JEM++: Improved Techniques for Training JEM
  ☆13Updated 2 years ago
• anniesch / jtt
  Code for "Just Train Twice: Improving Group Robustness without Training Group Information"
  ☆72Updated last year
• PolinaKirichenko / deep_feature_reweighting
  ☆107Updated last year
• izmailovpavel / spurious_feature_learning
  ☆45Updated 2 years ago
• AlexMeinke / certified-certain-uncertainty
  A way to achieve uniform confidence far away from the training data.
  ☆38Updated 4 years ago
• locuslab / perturbation_learning
  Learning perturbation sets for robust machine learning
  ☆65Updated 3 years ago
• harshays / inputgradients
  Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)
  ☆13Updated 2 years ago
• ermongroup / f-dre
  Featurized Density Ratio Estimation
  ☆20Updated 4 years ago
• tml-epfl / adv-training-corruptions
  On the effectiveness of adversarial training against common corruptions [UAI 2022]
  ☆30Updated 3 years ago
• abietti / kernel_reg
  Pytorch implementation of regularization methods for deep networks obtained via kernel methods.
  ☆22Updated 5 years ago
• gibipara92 / learning-explanations-hard-to-vary
  Code to implement the AND-mask and geometric mean to do gradient based optimization, from the paper "Learning explanations that are hard …
  ☆39Updated 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
• yaodongyu / BiasVariance-AdversarialTraining
  ☆8Updated 4 years ago
• nitarshan / robust-generalization-measures
  Official code for "In Search of Robust Measures of Generalization" (NeurIPS 2020)
  ☆28Updated 4 years ago
• tml-epfl / sharpness-vs-generalization
  A modern look at the relationship between sharpness and generalization [ICML 2023]
  ☆43Updated last year
• davidstutz / confidence-calibrated-adversarial-training
  Implementation of Confidence-Calibrated Adversarial Training (CCAT).
  ☆45Updated 4 years ago
• tml-epfl / understanding-sam
  Towards Understanding Sharpness-Aware Minimization [ICML 2022]
  ☆35Updated 3 years ago
• wgrathwohl / LSD
  Official Release of "Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling"
  ☆49Updated 5 years ago