Alternatives and similar repositories for Adv-SS-Pretraining

Users that are interested in Adv-SS-Pretraining are comparing it to the libraries listed below

• bfshi / InfoDrop
  Code for our paper "Informative Dropout for Robust Representation Learning: A Shape-bias Perspective" (ICML 2020)
  ☆125Updated 2 years ago
• garyzhao / ME-ADA
  "Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness" (NeurIPS 2020).
  ☆51Updated 4 years ago
• PKUAI26 / AT-CNN
  Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks
  ☆66Updated 5 years ago
• bethgelab / game-of-noise
  Trained model weights, training and evaluation code from the paper "A simple way to make neural networks robust against diverse image cor…
  ☆61Updated 2 years ago
• tingxueronghua / pytorch-classification-advprop
  ☆108Updated 3 years ago
• VITA-Group / Once-for-All-Adversarial-Training
  [NeurIPS 2020] "Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free" by Haotao Wang*, Tianlong C…
  ☆44Updated 3 years ago
• hendrycks / pre-training
  Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
  ☆100Updated 3 years ago
• hankook / SLA
  Self-supervised Label Augmentation via Input Transformations (ICML 2020)
  ☆105Updated 4 years ago
• LiYingwei / ShapeTextureDebiasedTraining
  Code and models for the paper Shape-Texture Debiased Neural Network Training (ICLR 2021)
  ☆108Updated last year
• mancinimassimiliano / pytorch_wbn
  PyTorch implementation of Weighted Batch-Normalization layers
  ☆37Updated 5 years ago
• snu-mllab / Co-Mixup
  Official PyTorch implementation of "Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity" (ICLR'21 Oral)
  ☆103Updated 3 years ago
• szq0214 / Un-Mix
  Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning.
  ☆151Updated 2 years ago
• Simon4Yan / Meta-set
  Automatic model evaluation (AutoEval) in CVPR'21&TPAMI'22
  ☆36Updated 2 years ago
• YU1ut / JointOptimization
  Code for 'Joint Optimization Framework for Learning with Noisy Labels'
  ☆38Updated 6 years ago
• meijieru / fast_advprop
  [ICLR 2022]: Fast AdvProp
  ☆35Updated 3 years ago
• DTennant / CL-Visualizing-Feature-Transformation
  Improving Contrastive Learning by Visualizing Feature Transformation, ICCV 2021 Oral
  ☆90Updated 3 years ago
• HaohanWang / HEX
  Example implementation for the paper: (ICLR Oral) Learning Robust Representations by Projecting Superficial Statistics Out
  ☆27Updated 4 years ago
• VITA-Group / CV_LTH_Pre-training
  [CVPR 2021] "The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models" Tianlong Chen, Jon…
  ☆69Updated 2 years ago
• Boyiliee / MoEx
  MoEx (Moment Exchange)
  ☆141Updated 4 years ago
• youjiangxu / FaMUS
  ☆28Updated 3 years ago
• UMBCvision / CompRess
  Compressing Representations for Self-Supervised Learning
  ☆78Updated 4 years ago
• yueatsprograms / uda_release
  Unsupervised Domain Adaptation through Self-Supervision
  ☆79Updated 3 years ago
• Newbeeyoung / Video-Corruption-Robustness
  ☆20Updated 3 years ago
• qinenergy / adanet
  [ICCV 2019 oral] Code for Semi-Supervised Learning by Augmented Distribution Alignment
  ☆62Updated 3 years ago
• P2333 / Reverse-Cross-Entropy
  Reverse Cross Entropy for Adversarial Detection (NeurIPS 2018)
  ☆45Updated 4 years ago
• cihangxie / SmoothAdversarialTraining
  Smooth Adversarial Training
  ☆67Updated 4 years ago
• HaohanWang / PAR_experiments
  Learning Robust Global Representations by Penalizing Local Predictive Power (NeurIPS 2019))
  ☆18Updated 2 years ago
• facebookresearch / augmentation-corruption
  This repository provides code for "On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness".
  ☆45Updated 2 years ago
• val-iisc / BPFC
  Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes
  ☆23Updated 5 years ago
• thuml / TransNorm
  Code release for "Transferable Normalization: Towards Improving Transferability of Deep Neural Networks" (NeurIPS 2019)
  ☆79Updated 4 years ago