gorkemalgan / deep_learning_with_noisy_labels_literature
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
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆222Updated 4 years ago
- NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels☆502Updated 3 years ago
- Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning☆554Updated 4 years ago
- A curated list of long-tailed recognition resources.☆583Updated last year
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆354Updated 5 years ago
- PseudoLabel 2013, VAT, PI model, Tempens, MeanTeacher, ICT, MixMatch, FixMatch☆448Updated 2 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
- Code for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels"☆170Updated 3 years ago
- Unofficial PyTorch Implementation of Unsupervised Data Augmentation.☆146Updated 4 years ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆126Updated last year
- Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels☆295Updated last year
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Updated 5 years ago
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆285Updated 3 years ago
- Reproduce Results for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels" https://arxiv.org/abs/1908.06112☆186Updated 4 years ago
- [NeurIPS 2020] This project provides a strong single-stage baseline for Long-Tailed Classification, Detection, and Instance Segmentation …☆565Updated 7 months ago
- Virtual Adversarial Training (VAT) implementation for PyTorch☆296Updated 6 years ago
- A collection of awesome things about mixed sample data augmentation☆131Updated 4 years ago
- A Survey☆552Updated 2 years ago
- The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks☆337Updated 6 years ago
- An update-to-date list for papers related with label-noise representation learning is here.☆89Updated 3 years ago
- ☆475Updated 5 months ago
- "In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning" by Mamshad Nayeem Riz…☆234Updated last year
- This repository contains code for the paper "Decoupling Representation and Classifier for Long-Tailed Recognition", published at ICLR 202…☆962Updated 3 years ago
- [NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss☆664Updated 3 years ago
- Variational Adversarial Active Learning (ICCV 2019)☆229Updated last year
- Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks☆324Updated 2 years ago
- [ ECCV 2020 Spotlight ] Pytorch implementation for "Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets"☆366Updated 2 years ago
- Code for "MixMatch - A Holistic Approach to Semi-Supervised Learning"☆643Updated last year
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆153Updated 4 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆84Updated 5 years ago