arXiv:2606.11695v1 Announce Type: new Abstract: High-quality labeled data is essential for training reliable ML/DL models. However, real-world datasets often contain a considerable proportion of corrupted labels, which can severely degrade model performance. To address this problem, we propose CANOLA, a novel framework for correcting corrupted labels through noise-aware learning and iterative label refinement. CANOLA explicitly estimates the underlying noise distribution of the dataset and incorporates this information into the training of a noise-aware Deep Neural Network. By incorporating no
The proliferation of large datasets for ML/DL models necessitates robust methods for handling data quality issues, making noise-aware learning a critical area of focus.
Reliable ML/DL model performance is directly tied to data quality; improvements in managing corrupted labels can significantly enhance the efficacy of AI systems across various applications.
The ability to systematically correct corrupted labels will lead to more robust and accurate AI models, reducing the overhead of manual data curation and improving trust in AI outcomes.
- · AI developers
- · Data scientists
- · Industries relying on ML/DL
- · Data annotation services (for tool integration)
- · Companies with poor data governance
- · Manual data cleaning processes (some aspects)
More accurate and reliable machine learning models will be deployed across various sectors.
Reduced need for extensive manual data cleaning, accelerating model development cycles and reducing costs.
Increased trust in AI systems could lead to broader adoption in sensitive applications previously hindered by data quality concerns.
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Read at arXiv cs.LG