The expenses involved in training state-of-the-art deep hashing retrieval
models have witnessed an increase due to the adoption of more sophisticated
models and large-scale datasets. Dataset Distillation (DD) or Dataset
Condensation(DC) focuses on generating smaller synthetic dataset that retains
the original information. Nevertheless, existing DD methods face challenges in
maintaining a trade-off between accuracy and efficiency. And the
state-of-the-art dataset distillation methods can not expand to all deep
hashing retrieval methods. In this paper, we propose an efficient condensation
framework that addresses these limitations by matching the feature-embedding
between synthetic set and real set. Furthermore, we enhance the diversity of
features by incorporating the strategies of early-stage augmented models and
multi-formation. Extensive experiments provide compelling evidence of the
remarkable superiority of our approach, both in terms of performance and
efficiency, compared to state-of-the-art baseline methods.

本文提出了一种有效的压缩框架，该框架通过匹配合成集和真实集之间的特征嵌入来增强特征的多样性，并结合早期增强模型和多形态的策略，显著提高了性能和效率，比现有的基准方法都要优越。