noisy-labels

Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

A curated list of resources for Learning with Noisy Labels

Curated list of open source tooling for data-centric AI on unstructured data.

A curated (most recent) list of resources for Learning with Noisy Labels

The toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling.

Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels

NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).

Code for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels"

[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels

Noise-Tolerant Paradigm for Training Face Recognition CNNs [Official, CVPR 2019]

The official implementation of the ACM MM'21 paper Co-learning: Learning from noisy labels with self-supervision.

NLNL: Negative Learning for Noisy Labels

[ICML2022 Long Talk] Official Pytorch implementation of "To Smooth or Not? When Label Smoothing Meets Noisy Labels"

ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

MoPro: Webly Supervised Learning

Adaptive Early-Learning Correction for Segmentation from Noisy Annotations (CVPR 2022 Oral)

The official code for the paper "Delving Deep into Label Smoothing", IEEE TIP 2021

This is a summary of research on noisy correspondence. There may be omissions. If anything is missing please get in touch with us. Our emails: linyijie.gm@gmail.com yangmouxing@gmail.com qinyang.gm@gmail.com

[NeurIPS 2020] Disentangling Human Error from the Ground Truth in Segmentation of Medical Images

PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"