Generalized Product Quantization Network for Semi-supervised Hashing
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Learning to hash has achieved great success in image retrieval due to its low storage cost and fast search speed. In recent years, hashing methods that take advantage of deep learning have come into the spotlight with some positive outcomes. However, these approaches do not meet expectations unless expensive label information is sufficient. To resolve this issue, we propose the first quantization-based semi-supervised hashing scheme: Generalized Product Quantization (GPQ) network. We design a novel metric learning strategy that preserves semantic similarity between labeled data, and employ entropy regularization term to fully exploit inherent potentials of unlabeled data. Our solution increases the generalization capacity of the hash function, which allows overcoming previous limitations in the retrieval community. Extensive experimental results demonstrate that GPQ yields state-of-the-art performance on large-scale real image benchmark datasets.
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