EricArazo / PseudoLabeling
Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"
☆153Updated 4 years ago
Related projects ⓘ
Alternatives and complementary repositories for PseudoLabeling
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆125Updated 5 years ago
- Meta-Learning based Noise-Tolerant Training☆123Updated 4 years ago
- ☆130Updated 2 years ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆126Updated last year
- [AAAI 2021] Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning☆135Updated 3 years ago
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆90Updated 3 years ago
- Pytorch implementation of Virtual Adversarial Training☆133Updated 5 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
- PyTorch implementation of consistency regularization methods for semi-supervised learning☆77Updated 4 years ago
- Improving Consistency-Based Semi-Supervised Learning with Weight Averaging☆185Updated 5 years ago
- Reproduce Results for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels" https://arxiv.org/abs/1908.06112☆184Updated 3 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆220Updated 4 years ago
- Code for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels"☆171Updated 3 years ago
- PyTorch implementation of Temporal Ensembling for Semi-Supervised Learning☆109Updated 6 years ago
- Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"☆48Updated 2 years ago
- Virtual Adversarial Training (VAT) implementation for PyTorch☆297Updated 5 years ago
- [ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels☆134Updated 4 months ago
- (NeurIPS 2020) Transductive Information Maximization for Few-Shot Learning https://arxiv.org/abs/2008.11297☆119Updated last year
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆117Updated last year
- Several SSL methods (Pi model, Mean Teacher) are implemented in pytorch☆81Updated 6 years ago
- Code for reproducing ICT (published in Neural Networks 2022, and in IJCAI 2019)☆144Updated 2 years ago
- Code for CVPR 2019 paper Label Propagation for Deep Semi-supervised Learning☆115Updated 4 years ago
- SKD : Self-supervised Knowledge Distillation for Few-shot Learning☆95Updated last year
- Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"☆80Updated 4 years ago
- Learning to Self-Train for Semi-Supervised Few-Shot☆93Updated last year
- Laplacian Regularized Few Shot Learning☆81Updated 2 years ago
- Variational Adversarial Active Learning (ICCV 2019)☆225Updated last year
- SSL-FEW-SHOT☆171Updated 4 years ago