[CIKM-2024] Official code for work "ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance"
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Updated
Jul 17, 2024 - Python
[CIKM-2024] Official code for work "ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance"
(CVPR 2024) Pytorch implementation of “SURE: SUrvey REcipes for building reliable and robust deep networks”
[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels
This repository implements conformal prediction methods for classification tasks that can automatically adapt to random label contamination in the calibration sample.
"A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?" (CVPR 2024)
[ICLR 2024] SemiReward: A General Reward Model for Semi-supervised Learning
Peerannot: classification for crowdsourced image datasets with Python
Effective and Robust Adversarial Training Against Data and Label Corruptions
A curated list of resources for Learning with Noisy Labels
Supplementary material and code for "Mitigating Label Noise through Data Ambiguation" as published at AAAI 2024.
A curated (most recent) list of resources for Learning with Noisy Labels
Challenging label noise called BadLabel; Robust label-noise learning called Robust DivideMix
Official PyTorch implementation of "Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data" (NeurIPS'23)
[NeurIPS 2023] "Combating Bilateral Edge Noise for Robust Link Prediction"
[NeurIPS 2023] Combating Bilateral Edge Noise for Robust Link Prediction
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]..
Hard Sample Aware Noise Robust Learning forHistopathology Image Classification
Code and data for the WWW 2021 research-track paper: Typing Errors in Factual Knowledge Graphs: Severity and Possible Ways Out
A Python Library for Biquality Learning
Learning algorithms for partially-known class-conditional label noise
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