tensorfreitas / Temporal-Ensembling-for-Semi-Supervised-LearningLinks
Implementation of Temporal Ensembling for Semi-Supervised Learning by Laine et al. with tensorflow eager execution
☆55Updated 6 years ago
Alternatives and similar repositories for Temporal-Ensembling-for-Semi-Supervised-Learning
Users that are interested in Temporal-Ensembling-for-Semi-Supervised-Learning are comparing it to the libraries listed below
Sorting:
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆155Updated 5 years ago
- Pytorch implementation of Virtual Adversarial Training☆134Updated 6 years ago
- TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER☆119Updated 8 years ago
- PyTorch implementation of Temporal Ensembling for Semi-Supervised Learning☆111Updated 7 years ago
- Meta-Learning based Noise-Tolerant Training☆123Updated 5 years ago
- Gold Loss Correction☆88Updated 6 years ago
- Virtual Adversarial Training (VAT) implementation for PyTorch☆296Updated 6 years ago
- Code released for ICML 2019 paper "Bridging Theory and Algorithm for Domain Adaptation".☆140Updated 6 years ago
- Improving Consistency-Based Semi-Supervised Learning with Weight Averaging☆188Updated 6 years ago
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆130Updated 6 years ago
- Several SSL methods (Pi model, Mean Teacher) are implemented in pytorch☆83Updated 7 years ago
- ☆130Updated 2 years ago
- Temporal ensembling for semi-supervised learning☆159Updated 8 years ago
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Updated 4 years ago
- Learning to Self-Train for Semi-Supervised Few-Shot☆94Updated 2 years ago
- Central Moment Discrepancy for Domain-Invariant Representation Learning (ICLR 2017, keras)☆69Updated 2 years ago
- Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018☆121Updated 5 years ago
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆354Updated 6 years ago
- Code release for Discriminative Adversarial Domain Adaptation (AAAI2020).☆119Updated 5 years ago
- "Learning to Discover Novel Visual Categories via Deep Transfer Clustering" by Kai Han, Andrea Vedaldi, Andrew Zisserman (ICCV 2019)☆169Updated 3 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆224Updated 5 years ago
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks☆231Updated 6 years ago
- Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"☆80Updated 5 years ago
- Learning Confidence for Out-of-Distribution Detection in Neural Networks☆276Updated 7 years ago
- Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018☆182Updated 5 years ago
- Learning What and Where to Transfer (ICML 2019)☆249Updated 5 years ago
- "Unified Deep Supervised Domain Adaptation and Generalization" (ICCV 2017)☆105Updated 5 years ago
- Unofficial PyTorch Implementation of Unsupervised Data Augmentation.☆148Updated 5 years ago
- Code release for Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation (ICML 2019)☆64Updated 6 years ago
- Implementation of Adversarial Domain Adaptation with Domain Mixup (AAAI 2020 Oral).☆162Updated 5 years ago