chenpf1025 / noisy_label_understanding_utilizing
ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
☆90Updated 4 years ago
Alternatives and similar repositories for noisy_label_understanding_utilizing:
Users that are interested in noisy_label_understanding_utilizing are comparing it to the libraries listed below
- Meta-Learning based Noise-Tolerant Training☆123Updated 4 years ago
- ☆130Updated 2 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆118Updated last year
- ICML'19: How does Disagreement Help Generalization against Label Corruption?☆21Updated 5 years ago
- Joint Optimization Framework for Learning with Noisy Labels☆45Updated 6 years ago
- Pytorch implementation of Virtual Adversarial Training☆133Updated 5 years ago
- Tensorflow implementation of S4L: Self-Supervised Semi-Supervised Learning☆95Updated 5 years ago
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆153Updated 4 years ago
- Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"☆48Updated 2 years ago
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Updated 5 years ago
- TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER☆118Updated 7 years ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆126Updated last year
- Self-supervised Label Augmentation via Input Transformations (ICML 2020)☆104Updated 4 years ago
- This is a collection of Papers and Codes for Noisy Labels Problem.☆61Updated 6 years ago
- NeurIPS'18: Masking: A New Perspective of Noisy Supervision☆54Updated 6 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆222Updated 4 years ago
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆125Updated 5 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆83Updated 5 years ago
- Code for 'Joint Optimization Framework for Learning with Noisy Labels'☆38Updated 6 years ago
- Gold Loss Correction☆86Updated 6 years ago
- Improving Consistency-Based Semi-Supervised Learning with Weight Averaging☆185Updated 5 years ago
- Reproduction of Momentum Contrast for Unsupervised Visual Representation Learning☆120Updated 6 months ago
- Code for ICML2020 "Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation"☆90Updated 3 years ago
- A PyTorch Implementation of a Large Margin Deep Networks for Classification☆23Updated 5 years ago
- [ICCV 2019 oral] Code for Semi-Supervised Learning by Augmented Distribution Alignment☆62Updated 2 years ago
- Code for reproducing ICT (published in Neural Networks 2022, and in IJCAI 2019)☆144Updated 2 years ago
- Domain Generalization via Model-Agnostic Learning of Semantic Features☆146Updated last year
- 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
- Notes and tutorials on "Mutual information and self-supervised learning"☆39Updated 5 years ago
- Code for Paper ''Dual Student: Breaking the Limits of the Teacher in Semi-Supervised Learning'' [ICCV 2019]☆119Updated 4 years ago