  Deep learning has made remarkable progress recently, largely due to the
availability of large, well-labeled datasets. However, the training on such
datasets elevates costs and computational demands. To address this, various
techniques like coreset selection, dataset distillation, and dataset
quantization have been explored in the literature. Unlike traditional
techniques that depend on uniform sample distributions across different
classes, our research demonstrates that maintaining performance is feasible
even with uneven distributions. We find that for certain classes, the variation
in sample quantity has a minimal impact on performance. Inspired by this
observation, an intuitive idea is to reduce the number of samples for stable
classes and increase the number of samples for sensitive classes to achieve a
better performance with the same sampling ratio. Then the question arises: how
can we adaptively select samples from a dataset to achieve optimal performance?
In this paper, we propose a novel active learning based adaptive sampling
strategy, Dataset Quantization with Active Learning based Adaptive Sampling
(DQAS), to optimize the sample selection. In addition, we introduce a novel
pipeline for dataset quantization, utilizing feature space from the final stage
of dataset quantization to generate more precise dataset bins. Our
comprehensive evaluations on the multiple datasets show that our approach
outperforms the state-of-the-art dataset compression methods.
