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When random label noise is added to a training dataset, the prediction errorof a neural network on a label-noise-free test dataset initially improvesduring early training but eventually deteriorates, following a U-shapeddependence on training time. This behaviour is believed to be a result ofneural networks learning the pattern of clean data first and fitting the noiselater in the training, a phenomenon that we refer to as clean-prioritylearning. In this study, we aim to explore the learning dynamics underlyingthis phenomenon. We theoretically demonstrate that, in the early stage oftraining, the update direction of gradient descent is determined by the cleansubset of training data, leaving the noisy subset has minimal to no impact,resulting in a prioritization of clean learning. Moreover, we show boththeoretically and experimentally, as the clean-priority learning goes on, thedominance of the gradients of clean samples over those of noisy samplesdiminishes, and finally results in a termination of the clean-priority learningand fitting of the noisy samples.

On Emergence of Clean-Priority Learning in Early Stopped Neural Networks