YisenWang / symmetric_cross_entropy_for_noisy_labels
Code for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels"
☆170Updated 3 years ago
Alternatives and similar repositories for symmetric_cross_entropy_for_noisy_labels:
Users that are interested in symmetric_cross_entropy_for_noisy_labels are comparing it to the libraries listed below
- Reproduce Results for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels" https://arxiv.org/abs/1908.06112☆186Updated 4 years ago
- [ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels☆136Updated 9 months ago
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Updated 5 years ago
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆126Updated 5 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆222Updated 4 years ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆126Updated last year
- ☆94Updated 4 years ago
- Meta-Learning based Noise-Tolerant Training☆124Updated 4 years ago
- When Does Label Smoothing Help?_pytorch_implementationimp☆125Updated 5 years ago
- Unofficial PyTorch Implementation of Unsupervised Data Augmentation.☆146Updated 4 years ago
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆153Updated 4 years ago
- ☆129Updated 2 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆84Updated 5 years ago
- A collection of awesome things about mixed sample data augmentation☆131Updated 4 years ago
- Regularizing Class-wise Predictions via Self-knowledge Distillation (CVPR 2020)☆107Updated 4 years ago
- paper "O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks" code☆78Updated 2 years ago
- This repo consists of collection of papers and repos on the topic of deep learning by noisy labels / label noise.☆233Updated 3 years ago
- Code for 'Joint Optimization Framework for Learning with Noisy Labels'☆38Updated 6 years ago
- [TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training☆129Updated 3 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated last year
- 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)☆96Updated 3 years ago
- Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels☆295Updated last year
- Code for Unsupervised Embedding Learning via Invariant and Spreading Instance Feature☆209Updated 5 years ago
- Self-supervised Label Augmentation via Input Transformations (ICML 2020)☆106Updated 4 years ago
- PyTorch implementation of “Negative Margin Matters: Understanding Margin in Few-shot Classification”☆150Updated 4 years ago
- PyTorch implementation of consistency regularization methods for semi-supervised learning☆78Updated 4 years ago
- [CVPR 2021] Adaptive Consistency Regularization for Semi-Supervised Transfer Learning☆103Updated 3 years ago
- [ ECCV 2020 Spotlight ] Pytorch implementation for "Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets"☆367Updated 2 years ago
- Virtual Adversarial Training (VAT) implementation for PyTorch☆296Updated 6 years ago