bhanML / Co-teachingLinks
NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
☆506Updated 3 years ago
Alternatives and similar repositories for Co-teaching
Users that are interested in Co-teaching are comparing it to the libraries listed below
Sorting:
- This repo consists of collection of papers and repos on the topic of deep learning by noisy labels / label noise.☆234Updated 3 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆223Updated 4 years ago
- Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning☆557Updated 4 years ago
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆352Updated 6 years ago
- [NeurIPS 2020] This project provides a strong single-stage baseline for Long-Tailed Classification, Detection, and Instance Segmentation …☆569Updated 10 months ago
- This repository contains code for the paper "Decoupling Representation and Classifier for Long-Tailed Recognition", published at ICLR 202…☆972Updated 3 years ago
- [NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss☆677Updated 3 years ago
- PseudoLabel 2013, VAT, PI model, Tempens, MeanTeacher, ICT, MixMatch, FixMatch☆451Updated 2 years ago
- A curated list of long-tailed recognition resources.☆584Updated 2 years ago
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆289Updated 3 years ago
- Code for "MixMatch - A Holistic Approach to Semi-Supervised Learning"☆651Updated last year
- Virtual Adversarial Training (VAT) implementation for PyTorch☆296Updated 6 years ago
- Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"☆798Updated last year
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Updated 5 years ago
- Code for paper "Learning to Reweight Examples for Robust Deep Learning"☆269Updated 6 years ago
- Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels☆296Updated 2 years ago
- [NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning☆754Updated 4 years ago
- Pytorch implementation for "Large-Scale Long-Tailed Recognition in an Open World" (CVPR 2019 ORAL)☆866Updated 2 years ago
- Code for reproducing Manifold Mixup results (ICML 2019)☆494Updated last year
- [ICLR2021 Oral] Free Lunch for Few-Shot Learning: Distribution Calibration☆473Updated 3 years ago
- [ ECCV 2020 Spotlight ] Pytorch implementation for "Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets"☆368Updated 2 years ago
- PyTorch code for "Prototypical Contrastive Learning of Unsupervised Representations"☆593Updated last month
- The official PyTorch implementation of paper BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition☆669Updated 2 years ago
- Variational Adversarial Active Learning (ICCV 2019)☆230Updated last year
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆128Updated 5 years ago
- Unofficial PyTorch Implementation of Unsupervised Data Augmentation.☆147Updated 4 years ago
- A Survey☆562Updated 2 years ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆126Updated last year
- Class-Balanced Loss Based on Effective Number of Samples. CVPR 2019☆612Updated 3 years ago
- ☆482Updated 8 months ago