xingruiyu / coteaching_plus
ICML'19 How does Disagreement Help Generalization against Label Corruption?
☆84Updated 5 years ago
Alternatives and similar repositories for coteaching_plus
Users that are interested in coteaching_plus are comparing it to the libraries listed below
Sorting:
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆128Updated 5 years ago
- Meta-Learning based Noise-Tolerant Training☆124Updated 4 years ago
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆34Updated 4 years ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆126Updated last year
- MoPro: Webly Supervised Learning☆87Updated last week
- Code for 'Joint Optimization Framework for Learning with Noisy Labels'☆38Updated 6 years ago
- Self-supervised Label Augmentation via Input Transformations (ICML 2020)☆106Updated 4 years ago
- ICML'19: How does Disagreement Help Generalization against Label Corruption?☆21Updated 5 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated last year
- Meta Label Correction for Noisy Label Learning☆85Updated 2 years ago
- PyTorch implementation of consistency regularization methods for semi-supervised learning☆78Updated 4 years ago
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Updated 4 years ago
- SwAV for CIFAR-10, adapted from https://github.com/facebookresearch/swav☆28Updated 3 years ago
- [ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels☆137Updated 10 months ago
- code for paper Decoupling "when to update" from "how to update" [https://arxiv.org/abs/1706.02613]☆21Updated 7 years ago
- [NeurIPS 2017] [ICML 2019] Code for complementary-label learning☆48Updated last year
- ☆129Updated 2 years ago
- NeurIPS'18: Masking: A New Perspective of Noisy Supervision☆54Updated 6 years ago
- Code for CVPR 2019 paper Label Propagation for Deep Semi-supervised Learning☆116Updated 4 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆222Updated 4 years ago
- Adjust Decision Boundary for Class Imbalanced Learning☆19Updated 4 years ago
- SKD : Self-supervised Knowledge Distillation for Few-shot Learning☆97Updated last year
- Official code for the paper "Meta Soft Label Generation for Noisy Labels" accepted at ICPR 2020.☆19Updated 4 years ago
- This is a collection of Papers and Codes for Noisy Labels Problem.☆63Updated 7 years ago
- ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"☆43Updated 2 years ago
- ☆61Updated 5 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
- SpotTune: Transfer Learning through Adaptive Fine-tuning☆90Updated 5 years ago
- Improving Calibration for Long-Tailed Recognition (CVPR2021)☆148Updated 3 years ago
- PyTorch implementation of the paper "SuperLoss: A Generic Loss for Robust Curriculum Learning" in NIPS 2020.☆29Updated 4 years ago