  Dataset distillation is a newly emerging task that synthesizes a small-size
dataset used in training deep neural networks (DNNs) for reducing data storage
and model training costs. The synthetic datasets are expected to capture the
essence of the knowledge contained in real-world datasets such that the former
yields a similar performance as the latter. Recent advancements in distillation
methods have produced notable improvements in generating synthetic datasets.
However, current state-of-the-art methods treat the entire synthetic dataset as
a unified entity and optimize each synthetic instance equally. This static
optimization approach may lead to performance degradation in dataset
distillation. Specifically, we argue that static optimization can give rise to
a coupling issue within the synthetic data, particularly when a larger amount
of synthetic data is being optimized. This coupling issue, in turn, leads to
the failure of the distilled dataset to extract the high-level features learned
by the deep neural network (DNN) in the latter epochs.
  In this study, we propose a new dataset distillation strategy called
Sequential Subset Matching (SeqMatch), which tackles this problem by adaptively
optimizing the synthetic data to encourage sequential acquisition of knowledge
during dataset distillation. Our analysis indicates that SeqMatch effectively
addresses the coupling issue by sequentially generating the synthetic
instances, thereby enhancing its performance significantly. Our proposed
SeqMatch outperforms state-of-the-art methods in various datasets, including
SVNH, CIFAR-10, CIFAR-100, and Tiny ImageNet. Our code is available at
https://github.com/shqii1j/seqmatch.
