  When random label noise is added to a training dataset, the prediction error
of a neural network on a label-noise-free test dataset initially improves
during early training but eventually deteriorates, following a U-shaped
dependence on training time. This behaviour is believed to be a result of
neural networks learning the pattern of clean data first and fitting the noise
later in the training, a phenomenon that we refer to as clean-priority
learning. In this study, we aim to explore the learning dynamics underlying
this phenomenon. We theoretically demonstrate that, in the early stage of
training, the update direction of gradient descent is determined by the clean
subset of training data, leaving the noisy subset has minimal to no impact,
resulting in a prioritization of clean learning. Moreover, we show both
theoretically and experimentally, as the clean-priority learning goes on, the
dominance of the gradients of clean samples over those of noisy samples
diminishes, and finally results in a termination of the clean-priority learning
and fitting of the noisy samples.
