  Coreset selection, which aims to select a subset of the most informative
training samples, is a long-standing learning problem that can benefit many
downstream tasks such as data-efficient learning, continual learning, neural
architecture search, active learning, etc. However, many existing coreset
selection methods are not designed for deep learning, which may have high
complexity and poor generalization performance. In addition, the recently
proposed methods are evaluated on models, datasets, and settings of different
complexities. To advance the research of coreset selection in deep learning, we
contribute a comprehensive code library, namely DeepCore, and provide an
empirical study on popular coreset selection methods on CIFAR10 and ImageNet
datasets. Extensive experiments on CIFAR10 and ImageNet datasets verify that,
although various methods have advantages in certain experiment settings, random
selection is still a strong baseline.
