Early stopping may not always be beneficial with noisy labels, study finds

A new research paper explores the effectiveness of early stopping in machine learning models when faced with noisy labels. The study, which uses a synthetic noisy classification task and experiments on CIFAR-10, suggests that in certain regimes, particularly with deeper convolutional networks, interpolation (continuing training past the point where all labels are classified) can yield better generalization than early stopping. The research introduces a phase diagram to illustrate how the balance between learning shared relationships and fitting noise influences generalization error across different kernel types and training times. AI

IMPACT This research could influence training strategies for models dealing with imperfect datasets, potentially leading to improved performance in real-world applications where label noise is common.

RANK_REASON Academic paper on a theoretical ML concept. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• CIFAR-10
• Fabrizio Boncoraglio

• paper
• other

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