A curated list of resources for Learning with Noisy Labels

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

Human annotated noisy labels for CIFAR-10 and CIFAR-100. The website of CIFAR-N is available at http://www.noisylabels.com/.

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

[TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training

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

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

[CVPR 2021] Code for "Augmentation Strategies for Learning with Noisy Labels".

Tensorflow source code for "CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise" (CVPR 2018)

[ICLR2021] Official Pytorch implementation of "When Optimizing f-Divergence is Robust with Label noise"

[ICLR 2024] SemiReward: A General Reward Model for Semi-supervised Learning

“SURE: SUrvey REcipes for building reliable and robust deep networks” (CVPR 2024) & (ECCV 2024 OOD-CV Challenge Winner)

Learning to Split for Automatic Bias Detection

ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"

AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise

Hard Sample Aware Noise Robust Learning forHistopathology Image Classification

Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters

AAAI 2021: Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels

[CIKM-2024] Official code for work "ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance"