arXiv:2606.07086v1 Announce Type: cross Abstract: Deep neural networks (DNNs) excel in computer vision tasks given large annotated datasets. In real-world applications, however, labels are often corrupted by ambiguity, human error, or dynamic environments. Over-parameterized DNNs easily memorize these noisy labels during training, degrading model accuracy and generalization. Existing data-cleaning and sample-selection strategies often rely on manually specified thresholds, prior knowledge of the noise ratio, or a single metric (either learning dynamics or geometric structure), making them unst
The proliferation of deep neural networks in real-world applications has brought the issue of noisy data to the forefront, as current methods are often insufficient or require manual intervention.
Improved data cleaning frameworks directly enhance the reliability and generalization of AI models, which is crucial for their effective deployment in critical systems and pervasive applications.
The ability to automatically and adaptively clean noisy datasets will accelerate AI development and reduce the operational overhead associated with data quality management.
- · AI developers
- · Data scientists
- · Industries relying on large datasets
- · AI infrastructure providers
- · Companies with poor data governance
- · Manual data annotation services
- · AI models prone to memorization
- · Developers using static data cleaning methods
More robust and accurate AI models will be deployed across various sectors.
This will lead to increased trust in AI systems and accelerate their adoption in sensitive applications.
The reduced need for perfect data could lower barriers to entry for AI development, fostering broader innovation but also potentially introducing new vectors for bias if not properly managed.
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Read at arXiv cs.LG