On Cross-Validation for Hyperparameter Optimization of Deep Learning Image Classifiers
Why cross-validation is the superior choice for optimizing deep learning models in data-starved domains.
Hyperparameter optimization (HPO) can materially affect the performance of deep learning (DL) image classifiers, but there is little empirical guidance on how to derive the validation signal that drives it, especially for the small sample sizes common in fields such as medical im...
ArXiv CS.CV
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This research provides critical empirical evidence that cross-validation significantly stabilizes hyperparameter tuning for data-constrained environments like medical imaging. By reducing performance-estimation error, practitioners can better ensure that model configurations chosen during development will generalize reliably to unseen clinical data.
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