Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching
This work addresses efficiency for big data retrieval applications, but it is incremental as it adapts existing condensation techniques to a specific domain.
The paper tackles the high training cost of deep hashing retrieval by applying dataset condensation methods, proposing IEM to enhance feature matching and showing superior performance and efficiency in experiments.
Deep hashing retrieval has gained widespread use in big data retrieval due to its robust feature extraction and efficient hashing process. However, training advanced deep hashing models has become more expensive due to complex optimizations and large datasets. Coreset selection and Dataset Condensation lower overall training costs by reducing the volume of training data without significantly compromising model accuracy for classification task. In this paper, we explore the effect of mainstream dataset condensation methods for deep hashing retrieval and propose IEM (Information-intensive feature Embedding Matching), which is centered on distribution matching and incorporates model and data augmentation techniques to further enhance the feature of hashing space. Extensive experiments demonstrate the superior performance and efficiency of our approach.