DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning
This work provides a tool for researchers to compare coreset selection methods in deep learning, but it is incremental as it focuses on benchmarking rather than proposing new algorithms.
The authors tackled the problem of coreset selection in deep learning by introducing DeepCore, a comprehensive library for evaluating methods, and found through experiments on CIFAR10 and ImageNet that random selection remains a strong baseline despite advantages of other methods in specific settings.
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.