Alternatives and similar repositories for fully-invertible-revnet

Users that are interested in fully-invertible-revnet are comparing it to the libraries listed below

• MadryLab / spatial-pytorch
  Codebase for "Exploring the Landscape of Spatial Robustness" (ICML'19, https://arxiv.org/abs/1712.02779).
  ☆26Updated 5 years ago
• ColinQiyangLi / LConvNet
  Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)
  ☆35Updated 4 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
• locuslab / projected_sinkhorn
  ☆87Updated 10 months ago
• hendrycks / pre-training
  Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
  ☆100Updated 3 years ago
• jonasrauber / linear-region-attack
  A powerful white-box adversarial attack that exploits knowledge about the geometry of neural networks to find minimal adversarial perturb…
  ☆12Updated 4 years ago
• chingyaoc / fair-mixup
  ICLR 2021, Fair Mixup: Fairness via Interpolation
  ☆56Updated 3 years ago
• revbucket / geometric-certificates
  Geometric Certifications of Neural Nets
  ☆41Updated 2 years ago
• max-andr / cross-lipschitz
  Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation [NeurIPS 2017]
  ☆18Updated 7 years ago
• ajiljalal / manifold-defense
  ☆21Updated 10 months ago
• AlexMeinke / certified-certain-uncertainty
  A way to achieve uniform confidence far away from the training data.
  ☆38Updated 4 years ago
• MadryLab / BREEDS-Benchmarks
  ☆55Updated 4 years ago
• neale / HyperGAN
  Generative Model for Neural Networks
  ☆24Updated 4 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
• ucla-vision / information-dropout
  Implementation of Information Dropout
  ☆39Updated 7 years ago
• yangarbiter / robust-local-lipschitz
  A Closer Look at Accuracy vs. Robustness
  ☆88Updated 4 years ago
• HaohanWang / HEX
  Example implementation for the paper: (ICLR Oral) Learning Robust Representations by Projecting Superficial Statistics Out
  ☆27Updated 4 years ago
• torrvision / whoneedsadversaries
  Code implementing the experiments described in the NeurIPS 2018 paper "With Friends Like These, Who Needs Adversaries?".
  ☆13Updated 4 years ago
• locuslab / robust_union
  [ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models.
  ☆26Updated 10 months ago
• MadryLab / robust-features-code
  Code for "Robustness May Be at Odds with Accuracy"
  ☆91Updated 2 years ago
• JonasGeiping / fullbatchtraining
  Training vision models with full-batch gradient descent and regularization
  ☆37Updated 2 years ago
• abietti / kernel_reg
  Pytorch implementation of regularization methods for deep networks obtained via kernel methods.
  ☆22Updated 5 years ago
• ishansd / swg
  Sliced Wasserstein Generator
  ☆38Updated 7 years ago
• PKUAI26 / AT-CNN
  Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks
  ☆66Updated 5 years ago
• wronnyhuang / gen-viz
  Code for the paper "Understanding Generalization through Visualizations"
  ☆60Updated 4 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
• cetmann / robustness-interpretability
  Code for the Paper 'On the Connection Between Adversarial Robustness and Saliency Map Interpretability' by C. Etmann, S. Lunz, P. Maass, …
  ☆16Updated 6 years ago
• alvinchangw / JARN_ICLR2020
  Implementation for Jacobian Adversarially Regularized Networks for Robustness (ICLR 2020)
  ☆22Updated 5 years ago
• MadryLab / adversarial_spatial
  Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.
  ☆49Updated 5 years ago
• BioroboticsLab / IBA-paper-code
  Code for the Paper "Restricting the Flow: Information Bottlenecks for Attribution"
  ☆77Updated 5 years ago