Alternatives and similar repositories for temporal-ensembling-semi-supervised

Users that are interested in temporal-ensembling-semi-supervised are comparing it to the libraries listed below

• vfdev-5 / UDA-pytorch
  Unsupervised Data Augmentation experiments in PyTorch
  ☆60Updated 5 years ago
• jackyyy0228 / Order-free-Learning-Alleviating-Exposure-Bias-in-Multi-label-Classification
  ☆20Updated 5 years ago
• tensorfreitas / Temporal-Ensembling-for-Semi-Supervised-Learning
  Implementation of Temporal Ensembling for Semi-Supervised Learning by Laine et al. with tensorflow eager execution
  ☆55Updated 5 years ago
• chenpf1025 / noisy_label_understanding_utilizing
  ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
  ☆91Updated 4 years ago
• cyber-meow / PUbiasedN
  PyTorch implementation for the paper Classification from Positive, Unlabeled and Biased Negative Data.
  ☆19Updated last year
• nolfwin / symloss-ber-auc
  Code for the paper: On Symmetric Losses for Learning from Corrupted Labels
  ☆19Updated 6 years ago
• 9310gaurav / virtual-adversarial-training
  Pytorch implementation of Virtual Adversarial Training
  ☆134Updated 6 years ago
• naoto0804 / pytorch-VAT
  Unofficial pytorch implementation of a paper, Distributional Smoothing with Virtual Adversarial Training [Miyato+, ICLR2016].
  ☆26Updated 7 years ago
• GuokaiLiu / Noisy-Labels-Problem-Collection
  This is a collection of Papers and Codes for Noisy Labels Problem.
  ☆63Updated 7 years ago
• dbrojas / domain_adapt
  Domain adaptation networks for digit recognitioning
  ☆14Updated 7 years ago
• SeongokRyu / mutual_information_and_self-supervised_learning
  Notes and tutorials on "Mutual information and self-supervised learning"
  ☆39Updated 5 years ago
• kevinlyu / Wasserstein-Adversarial-Domain-Adaptation
  Wasserstein Based Domain Adaptation Model
  ☆46Updated 6 years ago
• PhamNguyen97 / TSA_pytorch
  Training Signal Annealing
  ☆13Updated 5 years ago
• udibr / noisy_labels
  TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER
  ☆118Updated 8 years ago
• MatanBN / XRTransfer
  Code and dataset for "Transfer Learning Between Related Tasks Using Expected Label Proportions"
  ☆16Updated 5 years ago
• timerstime / SDG4DA
  Selection Distribution Generator for Domain Adaptation
  ☆27Updated 5 years ago
• vtddggg / ATDA
  Unofficial Implement of Asymmetric Tri-training for Unsupervised Domain Adaptation
  ☆40Updated 8 years ago
• geonm / tf_angular_loss
  Tensorflow Implementation of Deep Metric Learning with Angular Loss
  ☆17Updated 6 years ago
• hrayrhar / limit-label-memorization
  Improving generalization by controlling label-noise information in neural network weights.
  ☆40Updated 4 years ago
• P1Analytics / Pseudo-Label-for-Deep-Neural-Networks
  Semi-Supervised Learning Method for Deep Neural Networks
  ☆28Updated 7 years ago
• emalach / UpdateByDisagreement
  code for paper Decoupling "when to update" from "how to update" [https://arxiv.org/abs/1706.02613]
  ☆21Updated 7 years ago
• gitabcworld / skiprnn_pytorch
  A pytorch implementation of the paper: "Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks"
  ☆81Updated 6 years ago
• moskomule / pytorch.snapshot.ensembles
  PyTorch implementation of "SNAPSHOT ENSEMBLES: TRAIN 1, GET M FOR FREE" [WIP]
  ☆36Updated 8 years ago
• xjtushujun / Meta-weight-net_class-imbalance
  NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for class imbalance).
  ☆33Updated 5 years ago
• sKamiJ / DCCS
  Official PyTorch implementation for ECCV'20 paper: Deep Image Clustering with Category-Style Representation
  ☆16Updated 4 years ago
• geosada / PI
  Π Model from “Temporal Ensembling for Semi-Supervised Learning” (ICLR 2017) with TensorFlow.
  ☆16Updated 7 years ago
• mmazeika / glc
  Gold Loss Correction
  ☆87Updated 6 years ago
• PAN001 / Deep-Neural-Network-Ensembles-for-Extreme-Classification
  Deep Neural Network Ensembles for Extreme Classification
  ☆41Updated 6 years ago
• sseung0703 / Zero-shot_Knowledge_Distillation
  Zero-Shot Knowledge Distillation in Deep Networks in ICML2019
  ☆49Updated 6 years ago
• Manuscrit / Area-Under-the-Margin-Ranking
  Implementation of the paper Identifying Mislabeled Data using the Area Under the Margin Ranking: https://arxiv.org/pdf/2001.10528v2.pdf
  ☆21Updated 5 years ago