There are 3 repositories under label-noise topic.
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
[ICML2022 Long Talk] Official Pytorch implementation of "To Smooth or Not? When Label Smoothing Meets Noisy Labels"
The official implementation of the ACM MM'21 paper Co-learning: Learning from noisy labels with self-supervision.
[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
(CVPR 2024) Pytorch implementation of “SURE: SUrvey REcipes for building reliable and robust deep networks”
AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters
Hard Sample Aware Noise Robust Learning forHistopathology Image Classification
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"
Official PyTorch implementation of "Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data" (NeurIPS'23)
A Python Library for Biquality Learning
Official implementation of "An Action Is Worth Multiple Words: Handling Ambiguity in Action Recognition", BMVC 2022
[NeurIPS 2023] Combating Bilateral Edge Noise for Robust Link Prediction
Code repository for the robust active label correction paper.
In the context of Deep Learning: What is the right way to conduct example weighting? How do you understand loss functions and so-called theorems on them?
[NeurIPS 2023] "Combating Bilateral Edge Noise for Robust Link Prediction"
Challenging label noise called BadLabel; Robust label-noise learning called Robust DivideMix
Contains my experiments for the Game of Deep Learning Hackathon conducted by Analytics Vidhya
"A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?" (CVPR 2024)
Extra bits of unsanitized code for plotting, training, etc. related to our CVPR 2021 paper "Augmentation Strategies for Learning with Noisy Labels".
Code and data for the WWW 2021 research-track paper: Typing Errors in Factual Knowledge Graphs: Severity and Possible Ways Out
Double Descent results for FCNNs on MNIST, extended by Label Noise (Reconciling Modern Machine-Learning Practice and the Classical Bias–Variance Trade-Off) [Python/PyTorch]..