Alternatives and similar repositories for virtual-adversarial-training

Users that are interested in virtual-adversarial-training are comparing it to the libraries listed below

• lyakaap / VAT-pytorch
  Virtual Adversarial Training (VAT) implementation for PyTorch
  ☆296Updated 6 years ago
• thuml / Transferable-Adversarial-Training
  Code release for Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers (ICML2019)
  ☆81Updated 6 years ago
• benathi / fastswa-semi-sup
  Improving Consistency-Based Semi-Supervised Learning with Weight Averaging
  ☆188Updated 6 years ago
• LiJunnan1992 / MLNT
  Meta-Learning based Noise-Tolerant Training
  ☆123Updated 5 years ago
• ricvolpi / generalize-unseen-domains
  Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018
  ☆121Updated 5 years ago
• ferretj / temporal-ensembling
  PyTorch implementation of Temporal Ensembling for Semi-Supervised Learning
  ☆111Updated 7 years ago
• chenpf1025 / noisy_label_understanding_utilizing
  ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
  ☆91Updated 4 years ago
• Newbeeer / L_DMI
  Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"
  ☆119Updated 2 years ago
• danieltan07 / learning-to-reweight-examples
  PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning
  ☆354Updated 6 years ago
• vikasverma1077 / ICT
  Code for reproducing ICT (published in Neural Networks 2022, and in IJCAI 2019)
  ☆148Updated 3 years ago
• udibr / noisy_labels
  TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER
  ☆119Updated 8 years ago
• Mid-Push / Moving-Semantic-Transfer-Network
  Tensorflow codes for ICML2018, Learning Semantic Representations for Unsupervised Domain Adaptation
  ☆110Updated 7 years ago
• PaulAlbert31 / LabelNoiseCorrection
  Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction
  ☆224Updated 5 years ago
• yogeshbalaji / Generate_To_Adapt
  Implementation of "Generate To Adapt: Aligning Domains using Generative Adversarial Networks"
  ☆143Updated 6 years ago
• xinzheli1217 / learning-to-self-train
  Learning to Self-Train for Semi-Supervised Few-Shot
  ☆94Updated 2 years ago
• EricArazo / PseudoLabeling
  Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"
  ☆155Updated 5 years ago
• jik0730 / VAT-pytorch
  Virtual Adversarial Training (VAT) for semi-supervised MNIST written in PyTorch: https://arxiv.org/abs/1704.03976
  ☆25Updated 6 years ago
• mmazeika / glc
  Gold Loss Correction
  ☆88Updated 7 years ago
• ildoonet / unsupervised-data-augmentation
  Unofficial PyTorch Implementation of Unsupervised Data Augmentation.
  ☆148Updated 5 years ago
• peisuke / MomentumContrast.pytorch
  Reproduction of Momentum Contrast for Unsupervised Visual Representation Learning
  ☆120Updated last year
• alinlab / L2T-ww
  Learning What and Where to Transfer (ICML 2019)
  ☆250Updated 5 years ago
• VisionLearningGroup / DAL
  Domain agnostic learning with disentangled representations
  ☆147Updated 5 years ago
• RuiShu / dirt-t
  A DIRT-T Approach to Unsupervised Domain Adaptation (ICLR 2018)
  ☆176Updated 7 years ago
• s-laine / tempens
  Temporal ensembling for semi-supervised learning
  ☆159Updated 8 years ago
• thuml / MDD
  Code released for ICML 2019 paper "Bridging Theory and Algorithm for Domain Adaptation".
  ☆140Updated 6 years ago
• ChrisAllenMing / Mixup_for_UDA
  Implementation of Adversarial Domain Adaptation with Domain Mixup (AAAI 2020 Oral).
  ☆163Updated 5 years ago
• perrying / realistic-ssl-evaluation-pytorch
  Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"
  ☆80Updated 5 years ago
• Britefury / self-ensemble-visual-domain-adapt
  Code repository for the small image experiments our paper 'Self-ensembling for Domain Adaptation'
  ☆194Updated 6 years ago
• google-research / remixmatch
  ☆130Updated 3 years ago
• xingruiyu / coteaching_plus
  ICML'19 How does Disagreement Help Generalization against Label Corruption?
  ☆90Updated 6 years ago