arXiv:2605.31090v1 Announce Type: cross Abstract: Mislabeled samples in training datasets severely degrade the performance of deep networks, as overparameterized models tend to memorize erroneous labels. We address this challenge by proposing a novel approach for mislabeled data detection that leverages training dynamics. Our method is grounded in the key observation that correctly labeled samples exhibit consistent entropy decrease during training, while mislabeled samples maintain relatively high entropy throughout the training process. Building on this insight, we introduce a signed entropy
The proliferation of large datasets and deep learning models has amplified the challenge of mislabeled data, making robust detection methods increasingly critical for model performance and reliability.
Improving the accuracy and robustness of AI models by efficiently identifying and correcting mislabeled data is crucial for their deployment in sensitive applications across various industries.
This novel method provides a more effective and potentially automated way to clean training datasets, directly enhancing the quality and trustworthiness of AI systems.
- · AI model developers
- · Data annotation services
- · Industries relying on AI accuracy (e.g., healthcare, finance)
- · AI systems prone to memorizing noisy data
- · Inefficient manual data cleaning processes
AI models trained on cleaner data will exhibit improved performance and generalization capabilities.
The cost and time associated with preparing high-quality datasets for AI training could significantly decrease.
Increased trust in AI systems due to enhanced data integrity may accelerate their adoption in critical decision-making roles.
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Read at arXiv cs.AI