#数据仓库#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
#自然语言处理#Curated list of open source tooling for data-centric AI on unstructured data.
#计算机科学#A curated (most recent) list of resources for Learning with Noisy Labels
#计算机科学#The toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling.
#计算机科学#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"
#计算机科学#[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels
#人脸识别#Noise-Tolerant Paradigm for Training Face Recognition CNNs [Official, CVPR 2019]
#Awesome#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
#计算机科学#Adaptive Early-Learning Correction for Segmentation from Noisy Annotations (CVPR 2022 Oral)
#计算机科学#The official code for the paper "Delving Deep into Label Smoothing", IEEE TIP 2021
[NeurIPS 2020] Disentangling Human Error from the Ground Truth in Segmentation of Medical Images
#计算机科学#PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"
