  Recent studies have demonstrated that gradient matching-based dataset
synthesis, or dataset condensation (DC), methods can achieve state-of-the-art
performance when applied to data-efficient learning tasks. However, in this
study, we prove that the existing DC methods can perform worse than the random
selection method when task-irrelevant information forms a significant part of
the training dataset. We attribute this to the lack of participation of the
contrastive signals between the classes resulting from the class-wise gradient
matching strategy. To address this problem, we propose Dataset Condensation
with Contrastive signals (DCC) by modifying the loss function to enable the DC
methods to effectively capture the differences between classes. In addition, we
analyze the new loss function in terms of training dynamics by tracking the
kernel velocity. Furthermore, we introduce a bi-level warm-up strategy to
stabilize the optimization. Our experimental results indicate that while the
existing methods are ineffective for fine-grained image classification tasks,
the proposed method can successfully generate informative synthetic datasets
for the same tasks. Moreover, we demonstrate that the proposed method
outperforms the baselines even on benchmark datasets such as SVHN, CIFAR-10,
and CIFAR-100. Finally, we demonstrate the high applicability of the proposed
method by applying it to continual learning tasks.
