gorkemalgan / deep_learning_with_noisy_labels_literatureLinks
This repo consists of collection of papers and repos on the topic of deep learning by noisy labels / label noise.
☆234Updated 3 years ago
Alternatives and similar repositories for deep_learning_with_noisy_labels_literature
Users that are interested in deep_learning_with_noisy_labels_literature are comparing it to the libraries listed below
Sorting:
- NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels☆510Updated 4 years ago
- Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning☆565Updated 4 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆223Updated 5 years ago
- PseudoLabel 2013, VAT, PI model, Tempens, MeanTeacher, ICT, MixMatch, FixMatch☆450Updated 2 years ago
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆290Updated 3 years ago
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆128Updated 5 years ago
- Code for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels"☆172Updated 4 years ago
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆354Updated 6 years ago
- A curated list of long-tailed recognition resources.☆586Updated 2 years ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆127Updated last year
- A collection of awesome things about mixed sample data augmentation☆132Updated 5 years ago
- ☆484Updated 10 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
- Reproduce Results for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels" https://arxiv.org/abs/1908.06112☆189Updated 4 years ago
- [NeurIPS 2020] This project provides a strong single-stage baseline for Long-Tailed Classification, Detection, and Instance Segmentation …☆569Updated last year
- Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels☆296Updated 2 years ago
- Virtual Adversarial Training (VAT) implementation for PyTorch☆295Updated 6 years ago
- [NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss☆684Updated 3 years ago
- An update-to-date list for papers related with label-noise representation learning is here.☆90Updated 4 years ago
- [ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels☆138Updated last year
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Updated 6 years ago
- Code for "MixMatch - A Holistic Approach to Semi-Supervised Learning"☆653Updated last year
- A Survey☆566Updated 2 years ago
- The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks☆339Updated 6 years ago
- 📜 Self-Supervised Learning from Images: Up-to-date reading list.☆202Updated 3 years ago
- Meta-Learning based Noise-Tolerant Training☆126Updated 5 years ago
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆155Updated 4 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆88Updated 6 years ago
- [ ECCV 2020 Spotlight ] Pytorch implementation for "Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets"☆370Updated 3 years ago
- This repository contains code for the paper "Decoupling Representation and Classifier for Long-Tailed Recognition", published at ICLR 202…☆975Updated 3 years ago