  Image retrieval methods that employ hashing or vector quantization have
achieved great success by taking advantage of deep learning. 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 image retrieval 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 quantization network, 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.
