Towards Efficient Deep Hashing Retrieval: Condensing Your Data via
  Feature-Embedding Matching
Tao Feng Jie Zhang Peizheng Wang Zhijie Wang
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.