Alternatives and similar repositories for fastswa-semi-sup

Users that are interested in fastswa-semi-sup are comparing it to the libraries listed below

• vikasverma1077 / ICT
  Code for reproducing ICT (published in Neural Networks 2022, and in IJCAI 2019)
  ☆149Updated 2 years ago
• Britefury / self-ensemble-visual-domain-adapt
  Code repository for the small image experiments our paper 'Self-ensembling for Domain Adaptation'
  ☆194Updated 5 years ago
• google-research / remixmatch
  ☆129Updated 2 years ago
• srebuffi / residual_adapters
  ☆171Updated 4 years ago
• peisuke / MomentumContrast.pytorch
  Reproduction of Momentum Contrast for Unsupervised Visual Representation Learning
  ☆120Updated last year
• LiJunnan1992 / MLNT
  Meta-Learning based Noise-Tolerant Training
  ☆126Updated 4 years ago
• 9310gaurav / virtual-adversarial-training
  Pytorch implementation of Virtual Adversarial Training
  ☆134Updated 6 years ago
• lyakaap / VAT-pytorch
  Virtual Adversarial Training (VAT) implementation for PyTorch
  ☆295Updated 6 years ago
• ferretj / temporal-ensembling
  PyTorch implementation of Temporal Ensembling for Semi-Supervised Learning
  ☆111Updated 6 years ago
• ricvolpi / generalize-unseen-domains
  Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018
  ☆121Updated 5 years ago
• RuiShu / dirt-t
  A DIRT-T Approach to Unsupervised Domain Adaptation (ICLR 2018)
  ☆175Updated 7 years ago
• alinlab / Confident_classifier
  Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018
  ☆182Updated 5 years ago
• Newbeeer / L_DMI
  Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"
  ☆119Updated 2 years ago
• ildoonet / unsupervised-data-augmentation
  Unofficial PyTorch Implementation of Unsupervised Data Augmentation.
  ☆147Updated 4 years ago
• perrying / realistic-ssl-evaluation-pytorch
  Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"
  ☆80Updated 5 years ago
• yogeshbalaji / Generate_To_Adapt
  Implementation of "Generate To Adapt: Aligning Domains using Generative Adversarial Networks"
  ☆142Updated 6 years ago
• maple-research-lab / EnAET
  EnAET: Self-Trained Ensemble AutoEncoding Transformations for Semi-Supervised Learning
  ☆82Updated last year
• alinlab / L2T-ww
  Learning What and Where to Transfer (ICML 2019)
  ☆248Updated 4 years ago
• hendrycks / ss-ood
  Self-Supervised Learning for OOD Detection (NeurIPS 2019)
  ☆267Updated 4 years ago
• fmcarlucci / JigenDG
  Repository for the CVPR19 oral paper "Domain Generalization by Solving Jigsaw Puzzles"
  ☆252Updated 2 years ago
• google-research / s4l
  Tensorflow implementation of S4L: Self-Supervised Semi-Supervised Learning
  ☆94Updated 5 years ago
• vikasverma1077 / manifold_mixup
  Code for reproducing Manifold Mixup results (ICML 2019)
  ☆493Updated last year
• biomedia-mira / masf
  Domain Generalization via Model-Agnostic Learning of Semantic Features
  ☆148Updated 2 years ago
• EricArazo / PseudoLabeling
  Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"
  ☆153Updated 4 years ago
• Philip-Bachman / amdim-public
  Public repo for Augmented Multiscale Deep InfoMax representation learning
  ☆400Updated 5 years ago
• VisionLearningGroup / DAL
  Domain agnostic learning with disentangled representations
  ☆147Updated 5 years ago
• yikun2019 / PENCIL
  PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.
  ☆139Updated 6 years ago
• Mid-Push / Moving-Semantic-Transfer-Network
  Tensorflow codes for ICML2018, Learning Semantic Representations for Unsupervised Domain Adaptation
  ☆110Updated 7 years ago
• uoguelph-mlrg / confidence_estimation
  Learning Confidence for Out-of-Distribution Detection in Neural Networks
  ☆275Updated 7 years ago
• ZHKKKe / DualStudent
  Code for Paper ''Dual Student: Breaking the Limits of the Teacher in Semi-Supervised Learning'' [ICCV 2019]
  ☆118Updated 4 years ago