  Data sampling acts as a pivotal role in training deep learning models.
However, an effective sampling schedule is difficult to learn due to the
inherently high dimension of parameters in learning the sampling schedule. In
this paper, we propose an AutoSampling method to automatically learn sampling
schedules for model training, which consists of the multi-exploitation step
aiming for optimal local sampling schedules and the exploration step for the
ideal sampling distribution. More specifically, we achieve sampling schedule
search with shortened exploitation cycle to provide enough supervision. In
addition, we periodically estimate the sampling distribution from the learned
sampling schedules and perturb it to search in the distribution space. The
combination of two searches allows us to learn a robust sampling schedule. We
apply our AutoSampling method to a variety of image classification tasks
illustrating the effectiveness of the proposed method.
