Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels
☆299May 22, 2023Updated 2 years ago
Alternatives and similar repositories for ELR
Users that are interested in ELR are comparing it to the libraries listed below
Sorting:
- Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning☆575Sep 14, 2020Updated 5 years ago
- [CVPR 2021] Code for "Augmentation Strategies for Learning with Noisy Labels".☆113Jan 9, 2022Updated 4 years ago
- PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"☆71Mar 30, 2021Updated 4 years ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆127Oct 24, 2023Updated 2 years ago
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆38Feb 24, 2021Updated 5 years ago
- ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels☆78Jun 15, 2021Updated 4 years ago
- ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"☆46Oct 29, 2022Updated 3 years ago
- Official Implementation of Robust Training under Label Noise by Over-parameterization☆66Sep 15, 2022Updated 3 years ago
- A curated list of resources for Learning with Noisy Labels☆2,721May 3, 2025Updated 10 months ago
- Adaptive Early-Learning Correction for Segmentation from Noisy Annotations (CVPR 2022 Oral)☆89Jun 19, 2022Updated 3 years ago
- [ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels☆141Jul 5, 2024Updated last year
- A curated (most recent) list of resources for Learning with Noisy Labels☆720Oct 18, 2024Updated last year
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆129Nov 12, 2019Updated 6 years ago
- [TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training☆130Oct 17, 2021Updated 4 years ago
- CVPR 2022: Selective-Supervised Contrastive Learning with Noisy Labels☆95Mar 28, 2022Updated 3 years ago
- This repo consists of collection of papers and repos on the topic of deep learning by noisy labels / label noise.☆236Sep 20, 2021Updated 4 years ago
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆35Feb 24, 2021Updated 5 years ago
- Official code for the paper "Meta Soft Label Generation for Noisy Labels" accepted at ICPR 2020.☆21Oct 12, 2020Updated 5 years ago
- Official implementation for: "Multi-Objective Interpolation Training for Robustness to Label Noise"☆41May 13, 2022Updated 3 years ago
- AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise☆35Jun 9, 2021Updated 4 years ago
- [CVPR'22] Official Implementation of the CVPR 2022 paper "UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learnin…☆63Oct 10, 2024Updated last year
- Reproduce Results for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels" https://arxiv.org/abs/1908.06112☆191Dec 27, 2020Updated 5 years ago
- ☆13Aug 25, 2020Updated 5 years ago
- ☆14Jan 7, 2023Updated 3 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆120Jun 6, 2023Updated 2 years ago
- NeurIPS'2020: Part-dependent Label Noise: Towards Instance-dependent Label Noise☆61Dec 16, 2020Updated 5 years ago
- noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation.☆59Aug 7, 2022Updated 3 years ago
- A Survey☆572Feb 13, 2023Updated 3 years ago
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Dec 10, 2020Updated 5 years ago
- NeurIPS 2020, "A Topological Filter for Learning with Label Noise".☆30Apr 11, 2025Updated 10 months ago
- Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning.☆150Aug 10, 2022Updated 3 years ago
- Human annotated noisy labels for CIFAR-10 and CIFAR-100. The website of CIFAR-N is available at http://www.noisylabels.com/.☆174May 17, 2023Updated 2 years ago
- ☆30Jan 7, 2023Updated 3 years ago
- Meta-Learning based Noise-Tolerant Training☆123Aug 16, 2020Updated 5 years ago
- Official repository for Reliable Label Bootstrapping☆19Mar 24, 2023Updated 2 years ago
- Learning with Noisy Labels via Sparse Regularization, ICCV2021☆46Apr 12, 2022Updated 3 years ago
- (L2ID@CVPR2021, TNNLS2022) Boosting Co-teaching with Compression Regularization for Label Noise☆51Feb 28, 2023Updated 3 years ago
- PyTorch implementation of the paper "SuperLoss: A Generic Loss for Robust Curriculum Learning" in NIPS 2020.☆29Jan 26, 2021Updated 5 years ago
- The official code for the NeurIPS 2021 paper Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels (https://arxiv.org…☆25Dec 29, 2021Updated 4 years ago