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. However, the quality of the real-world datasets is often compromised by noisy labels, which can substantially degrade model accuracy and reliability. To address this challenge, we propose Relabeler, an end-to-end data-centric framework for detecting and correcting corrupted labels. For corrupted label detection, Relabeler jointly leverages both local and global relationships among data instances to identify potentially noisy samples.
The proliferation of real-world datasets for machine learning, often acquired with less stringent quality control, makes effective label corruption detection and correction increasingly critical for model performance and reliability.
Improving data quality tools enhances the reliability and trustworthiness of AI models, directly impacting the efficacy of AI applications across various industries and reducing development costs associated with poor data.
The development of more robust data-centric frameworks like Relabeler shifts focus towards automated and efficient methods for maintaining high-quality training data, potentially democratizing access to performant AI models by mitigating the impact of noisy data.
- · AI developers
- · Companies with large, noisy datasets
- · Machine learning platforms
- · Data annotation services (those adopting quality tools)
- · Companies relying on low-quality data
- · Manual data cleaning services (without advanced tools)
AI models trained on real-world datasets will exhibit higher accuracy and robustness.
Reduced need for extensive manual data cleaning, accelerating AI development cycles and lowering barriers to entry for smaller teams.
Increased trust in AI systems could lead to broader adoption in sensitive applications previously hindered by concerns over data quality and model reliability.
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Read at arXiv cs.LG