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.
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.
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arXiv Machine Learning
Jun 11
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.
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.
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 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
Jul 16
Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels
arXiv:2604. 06614v2 Announce Type: replace-cross Abstract: Prompt learning has gained significant attention as a parameter-efficient approach for adapting large pre-trained vision-language models to downstream tasks.
arXiv Machine Learning
Jun 3
Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension
arXiv:2405. 03386v2 Announce Type: replace Abstract: Training with noisy class labels impairs neural networks' generalization performance.
arXiv Machine Learning
Jun 5
Addressing Imbalance in Multi-Label Data via Label-Specific Distance-based Oversampling
arXiv:2606. 05927v1 Announce Type: new Abstract: The complex imbalanced label distribution poses a crucial challenge to multi-label classification, as most classifiers are biased towards the majority class and high-frequent labels.
arXiv AI
Jun 11
CoVar: Confidence-Variance-Guided Pseudo-Label Selection for Semi-Supervised Learning
arXiv:2601. 11670v3 Announce Type: replace-cross Abstract: Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable under model overconfidence and class imbalance.
arXiv Machine Learning
Jul 27
The Role of Pseudo-labels in Self-training Linear Classifiers on High-dimensional Gaussian Mixture Data
arXiv:2205. 07739v4 Announce Type: replace-cross Abstract: Self-training (ST) is a simple yet effective semi-supervised learning method.
arXiv Machine Learning
Jul 7
Labeled-Data-Free Meta-Learning: Efficient Task Generation Using Pre-trained Models and Unlabeled Data
arXiv:2607. 02850v1 Announce Type: new Abstract: Meta-learning without labeled data is crucial for real-world applications, where obtaining labeled datasets can be expensive or restricted due to privacy concerns.
arXiv Machine Learning
Jul 14
Random Label Prediction Heads for Studying Memorization in Deep Neural Networks
arXiv:2607. 11541v1 Announce Type: new Abstract: We introduce a straightforward yet effective method to empirically study memorization in deep neural networks for classification tasks.