Alternatives and similar repositories for LRT:

Users that are interested in LRT are comparing it to the libraries listed below

• pxiangwu / PLC
  ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"
  ☆43Updated 2 years ago
• guixianjin / noisy_label_papers
  This repository is used to record current noisy label paper in mainstream ML and CV conference and journal.
  ☆35Updated 3 years ago
• AlanChou / Super-Loss
  PyTorch implementation of the paper "SuperLoss: A Generic Loss for Robust Curriculum Learning" in NIPS 2020.
  ☆30Updated 4 years ago
• Kthyeon / FINE_official
  NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"
  ☆39Updated 3 years ago
• mingkai-zheng / SimMatch
  SimMatch: Semi-supervised Learning with Similarity Matching
  ☆91Updated last year
• ShikunLi / Sel-CL
  CVPR 2022: Selective-Supervised Contrastive Learning with Noisy Labels
  ☆92Updated 3 years ago
• hongxin001 / JoCoR
  CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization
  ☆126Updated last year
• gorkemalgan / MSLG_noisy_label
  Official code for the paper "Meta Soft Label Generation for Noisy Labels" accepted at ICPR 2020.
  ☆19Updated 4 years ago
• UCSC-REAL / cores
  Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)
  ☆37Updated 4 years ago
• arghosh / noisy_label_pretrain
  ☆32Updated 4 years ago
• lblaoke / TLC
  PyTorch implementation of the paper "Trustworthy Long-Tailed Classification" (CVPR 2022)
  ☆61Updated last year
• sarsbug / SMP
  Pytorch implementation for Deep Self-Learning From Noisy Labels
  ☆33Updated 5 years ago
• kaidic / HAR
  [ICLR 2021] Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization
  ☆41Updated 4 years ago
• hitcszx / ALFs
  Learning with Noisy Labels, Label Noise, ICML 2021
  ☆44Updated 2 years ago
• LARS-research / S2E
  Q. Yao, H. Yang, B. Han, G. Niu, J. Kwok. Searching to Exploit Memorization Effect in Learning from Noisy Labels. ICML 2020
  ☆22Updated 4 years ago
• youjiangxu / FaMUS
  ☆28Updated 3 years ago
• bhanML / SIGUA
  ICML'20: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
  ☆15Updated 4 years ago
• VITA-Group / MAK
  [NeurIPS 2021] “Improving Contrastive Learning on Imbalanced Data via Open-World Sampling”, Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangya…
  ☆28Updated 3 years ago
• xdxuyang / ALRDC
  ☆16Updated 4 years ago
• PaulAlbert31 / PLS
  Official repository for Is your noise correction noisy? PLS: Robustness to label noise with two stage detection WACV 2023
  ☆20Updated 2 years ago
• by-liu / MbLS
  Code of our method MbLS (Margin-based Label Smoothing) for network calibration. To Appear at CVPR 2022. Paper : https://arxiv.org/abs/211…
  ☆50Updated 2 years ago
• salesforce / MoPro
  MoPro: Webly Supervised Learning
  ☆87Updated last week
• thuml / Batch-Spectral-Shrinkage
  Code release for Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning (NeurIPS 2019)
  ☆24Updated 3 years ago
• salesforce / RRL
  ☆17Updated last week
• YU1ut / Multi-Task-Curriculum-Framework-for-Open-Set-SSL
  Code for "Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning"
  ☆24Updated 4 years ago
• alibaba-edu / Ranking-based-Instance-Selection
  Ranking-based-Instance-Selection
  ☆32Updated 3 years ago
• xiangly55 / LFME
  Code for "Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification", ECCV 2020 Spotlight
  ☆38Updated 4 years ago
• takashiishida / comp
  [NeurIPS 2017] [ICML 2019] Code for complementary-label learning
  ☆48Updated last year
• pxiangwu / TopoFilter
  NeurIPS 2020, "A Topological Filter for Learning with Label Noise".
  ☆30Updated 3 weeks ago
• facebookresearch / noisy_few_shot
  Few-shot learning with mislabeled support samples
  ☆41Updated 2 years ago