tensorfreitas / Temporal-Ensembling-for-Semi-Supervised-LearningLinks
Implementation of Temporal Ensembling for Semi-Supervised Learning by Laine et al. with tensorflow eager execution
☆55Updated 5 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:
- TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER☆118Updated 8 years ago
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆153Updated 4 years ago
- ☆129Updated 2 years ago
- Pytorch implementation of Virtual Adversarial Training☆134Updated 6 years ago
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Updated 4 years ago
- PyTorch implementation of Temporal Ensembling for Semi-Supervised Learning☆111Updated 6 years ago
- Meta-Learning based Noise-Tolerant Training☆125Updated 4 years ago
- Temporal ensembling for semi-supervised learning☆158Updated 8 years ago
- Gold Loss Correction☆87Updated 6 years ago
- Virtual Adversarial Training (VAT) implementation for PyTorch☆296Updated 6 years ago
- This is a collection of Papers and Codes for Noisy Labels Problem.☆63Updated 7 years ago
- Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018☆181Updated 5 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆84Updated 5 years ago
- Several SSL methods (Pi model, Mean Teacher) are implemented in pytorch☆81Updated 6 years ago
- ICML'19: How does Disagreement Help Generalization against Label Corruption?☆21Updated 5 years ago
- Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"☆80Updated 5 years ago
- PyTorch implementation of consistency regularization methods for semi-supervised learning☆78Updated 4 years ago
- Sinkhorn Label Allocation is a label assignment method for semi-supervised self-training algorithms. The SLA algorithm is described in fu…☆53Updated 3 years ago
- Code for ICML2020 "Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation"☆90Updated 4 years ago
- Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018☆121Updated 5 years ago
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks☆230Updated 6 years ago
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆352Updated 6 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated last year
- Description Code for the paper "Robust Inference via Generative Classifiers for Handling Noisy Labels".☆33Updated 5 years ago
- Virtual Adversarial Training (VAT) for semi-supervised MNIST written in PyTorch: https://arxiv.org/abs/1704.03976☆25Updated 6 years ago
- Code for CVPR 2019 paper Label Propagation for Deep Semi-supervised Learning☆116Updated 4 years ago
- Improving Consistency-Based Semi-Supervised Learning with Weight Averaging☆186Updated 6 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆223Updated 4 years ago
- Unofficial PyTorch Implementation of Unsupervised Data Augmentation.☆147Updated 4 years ago
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Updated 5 years ago