Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching

• URL: http://arxiv.org/abs/2305.18076v1
• Date: Mon, 29 May 2023 13:23:55 GMT
• Title: Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching
• Authors: Tao Feng, Jie Zhang, Peizheng Wang, Zhijie Wang
• Abstract summary: The expenses involved in training state-of-the-art deep hashing retrieval models have witnessed an increase. The state-of-the-art dataset distillation methods can not expand to all deep hashing retrieval methods. We propose an efficient condensation framework that addresses these limitations by matching the feature-embedding between synthetic set and real set.
• Score: 7.908244841289913
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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