AlanChou / Truncated-LossLinks
PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018
☆128Updated 5 years ago
Alternatives and similar repositories for Truncated-Loss
Users that are interested in Truncated-Loss are comparing it to the libraries listed below
Sorting:
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆127Updated last year
- Reproduce Results for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels" https://arxiv.org/abs/1908.06112☆189Updated 4 years ago
- [ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels☆138Updated last year
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆88Updated 6 years ago
- [AAAI 2021] Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning☆139Updated 4 years ago
- Code for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels"☆172Updated 4 years ago
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆155Updated 4 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆223Updated 5 years ago
- Meta-Learning based Noise-Tolerant Training☆126Updated 5 years ago
- PyTorch implementation of consistency regularization methods for semi-supervised learning☆78Updated 5 years ago
- ☆129Updated 2 years ago
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Updated 6 years ago
- Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels☆296Updated 2 years ago
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆290Updated 3 years ago
- ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"☆43Updated 2 years ago
- When Does Label Smoothing Help?_pytorch_implementationimp☆125Updated 5 years ago
- A collection of awesome things about mixed sample data augmentation☆132Updated 5 years ago
- Regularizing Class-wise Predictions via Self-knowledge Distillation (CVPR 2020)☆108Updated 5 years ago
- Adjust Decision Boundary for Class Imbalanced Learning☆19Updated 5 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated 2 years ago
- Improving Calibration for Long-Tailed Recognition (CVPR2021)☆148Updated 3 years ago
- Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization☆128Updated 3 months ago
- "In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning" by Mamshad Nayeem Riz…☆236Updated 2 years ago
- [TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training☆129Updated 3 years ago
- SwAV for CIFAR-10, adapted from https://github.com/facebookresearch/swav☆28Updated 3 years ago
- "Automatically Discovering and Learning New Visual Categories with Ranking Statistics" by Kai Han, Sylvestre-Alvise Rebuffi, Sebastien Eh…☆229Updated 5 years ago
- Self-supervised Label Augmentation via Input Transformations (ICML 2020)☆105Updated 4 years ago
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Updated 4 years ago
- Code for the paper "M2m: Imbalanced Classification via Major-to-minor Translation" (CVPR 2020)☆95Updated 3 years ago
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆34Updated 4 years ago