arXiv AI
Jun 11
Noise-Aware Framework for Correcting Corrupted Labels
arXiv:2606. 11695v1 Announce Type: cross Abstract: High-quality labeled data is essential for training reliable ML/DL models.
A Data-Centric Framework for Detecting and Correcting Corrupted Labels
arXiv:2606. 11699v1 Announce Type: new Abstract: The performance of machine learning and deep learning models largely depends on the quality of the training data.
Related stories
arXiv Machine Learning
Jun 16
Benchmarking Instance-Dependent Label Noise with Controlled Corruptions
arXiv:2606. 14965v1 Announce Type: new Abstract: Synthetic instance-dependent label noise (IDN) benchmarks are widely used to evaluate noisy-label learning methods, yet existing approaches typically generate noise through imperfect annotators or classifier raters, leaving the source of ambiguity implicit.
arXiv Machine Learning
Aug 5
Stop Replacing Noise with Noise: Two-Source Reliability Assessment for Label Correction and Sample Reweighting in Label-Noise Learning
arXiv:2608. 03432v1 Announce Type: new Abstract: Refurbishment-based noisy-label learning mixes an observed label with a model-derived pseudo target, typically using one sample-wise cleanliness score to control both branches.
arXiv Machine Learning
Jun 8
An Adaptive Data cleaning Framework for Noisy Label Detection
arXiv:2606. 07086v1 Announce Type: cross Abstract: Deep neural networks (DNNs) excel in computer vision tasks given large annotated datasets.
arXiv Machine Learning
Jul 28
XMix: Combating Extremely Noisy Labels via Local Smoothness in Self-Supervised Feature Space
arXiv:2607. 23865v1 Announce Type: new Abstract: Supervised deep learning models rely on large, accurately labeled datasets, yet noisy annotations are often unavoidable and can severely degrade performance under high noise levels.
arXiv AI
Jun 9
Deep Active Re-Labeling: Toward Noise-Resilient Annotation Efficiency
arXiv:2606. 08718v1 Announce Type: cross Abstract: While Deep Active Learning (DAL) effectively reduces human annotation costs, its efficacy is constrained by human annotation errors.
arXiv Machine Learning
Jun 4
Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction
arXiv:2601. 17469v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have shown remarkable capabilities in learning from graph-structured data with various applications such as social analysis and bioinformatics.
arXiv Machine Learning
Jun 11
DeMix: Debugging Training Data with Mixed Data Error Types by Investigating Influence Vectors
arXiv:2606. 11616v1 Announce Type: new Abstract: High-quality training data is essential for the success of machine learning models.
arXiv AI
Jul 7
An automated method of identifying incorrectly labelled images based on the sequences of loss functions of deep learning networks
arXiv:2607. 02594v1 Announce Type: cross Abstract: Deep learning is widely applied in medical image analysis, but up to 10% of manually labelled images may be incorrect, degrading model performance.
arXiv Machine Learning
Jun 5
Towards Label-Noise Resistant Learning via Optimal Brain Damage Masking
arXiv:2508. 09697v3 Announce Type: replace Abstract: Noisy labels are inevitable in real-world scenarios.
arXiv Machine Learning
Jul 30
The Advantage of Fine-Grained Training
arXiv:2509. 05130v2 Announce Type: replace Abstract: In classification problems, models are trained to predict a class label based on the input data features.
arXiv Machine Learning
Jul 8
Reliable Mislabel Detection for Video Capsule Endoscopy Data
arXiv:2602. 06938v2 Announce Type: replace-cross Abstract: The classification performance of deep neural networks relies strongly on access to large, accurately annotated datasets.