ZIYU-DEEP / Awesome-Autoencoders-for-Representation-Learning

Related projects: ⓘ

• sayakpaul / par-cvpr-21
  Contains notebooks for the PAR tutorial at CVPR 2021.
  ☆36Updated 3 years ago
• stanislavfort / OpenAI_CLIP_adversarial_examples
  Developing adversarial examples and showing their semantic generalization for the OpenAI CLIP model (https://github.com/openai/CLIP)
  ☆26Updated 3 years ago
• sayakpaul / robustness-foundation-models
  This repository holds code and other relevant files for the NeurIPS 2022 tutorial: Foundational Robustness of Foundation Models.
  ☆70Updated last year
• locuslab / perturbation_learning
  Learning perturbation sets for robust machine learning
  ☆64Updated 3 years ago
• HymEric / Awesome-Representation-Learning-CV-PaperAndCode
  Awesome-Representation-Learning-CV-PaperAndCodes, lasted development in the representation learning area.
  ☆33Updated last year
• deepgenerativeclassifier / DeepBayes
  Code release for the ICML 2019 paper "Are generative classifiers more robust to adversarial attacks?"
  ☆23Updated 5 years ago
• arpitbansal297 / Meta-Balance
  ☆24Updated 2 years ago
• eth-sri / fnf
  ☆16Updated 2 years ago
• vcl-iisc / DeGAN
  Data-enriching GAN for retrieving Representative Samples from aTrained Classifier
  ☆12Updated 4 years ago
• js-d / sim_metric
  ☆32Updated 11 months ago
• LTS4 / hold-me-tight
  Source code of "Hold me tight! Influence of discriminative features on deep network boundaries"
  ☆22Updated 2 years ago
• facebookresearch / fisher_information_loss
  This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"
  ☆48Updated 3 years ago
• yunyuntsai / Black-box-Adversarial-Reprogramming
  Code for "Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources". (IC…
  ☆37Updated 3 years ago
• davidstutz / disentangling-robustness-generalization
  CVPR'19 experiments with (on-manifold) adversarial examples.
  ☆43Updated 4 years ago
• tonyduan / rs4a
  Randomized Smoothing of All Shapes and Sizes (ICML 2020).
  ☆50Updated 4 years ago
• columbia / Metric_Learning_Adversarial_Robustness
  Code for NeurIPS 2019 Paper
  ☆49Updated 4 years ago
• tml-epfl / adv-training-corruptions
  On the effectiveness of adversarial training against common corruptions [UAI 2022]
  ☆30Updated 2 years ago
• stanislavfort / adversaries_to_convnext
  Adversarial examples to the new ConvNeXt architecture
  ☆20Updated 2 years ago
• MadryLab / DebuggableDeepNetworks
  ☆37Updated 3 years ago
• yaodongyu / Rethink-BiasVariance-Tradeoff
  Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
  ☆49Updated 3 years ago
• peterbhase / interpretable-image
  Code for "Interpretable Image Recognition with Hierarchical Prototypes"
  ☆18Updated 4 years ago
• rehg-lab / CLRec
  Pytorch implementation for "The Surprising Positive Knowledge Transfer in Continual 3D Object Shape Reconstruction"
  ☆33Updated 2 years ago
• liaopeiyuan / GAL
  [ICML 2021] Information Obfuscation of Graph Neural Networks
  ☆36Updated 3 years ago
• JonasGeiping / dataaugs
  ☆17Updated last year
• j-cb / Breaking_Down_OOD_Detection
  ☆11Updated 2 years ago
• yashkant / Elastic-Weight-Consolidation
  This is an implementation of Elastic Weight Consolidation algorithm introduced in Overcoming catastrophic forgetting in neural networks.
  ☆53Updated 6 years ago
• IBM / CROWN-Robustness-Certification
  CROWN: A Neural Network Verification Framework for Networks with General Activation Functions
  ☆38Updated 5 years ago
• Harry24k / adversarial-defenses-pytorch
  PyTorch implementations of Adversarial defenses and utils.
  ☆34Updated 8 months ago
• snap-stanford / crust
  [NeurIPS 2020] Coresets for Robust Training of Neural Networks against Noisy Labels
  ☆33Updated 3 years ago
• ondrejbohdal / label-distillation
  Official PyTorch implementation of “Flexible Dataset Distillation: Learn Labels Instead of Images”
  ☆41Updated 3 years ago