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.
View on arXiv@article{feng2025_2305.18076,
title={ Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching },
author={ Tao Feng and Jie Zhang and Huashan Liu and Zhijie Wang and Shengyuan Pang },
journal={arXiv preprint arXiv:2305.18076},
year={ 2025 }
}