Abstract. Product quantization has been widely used in fast image
retrieval due to its effectiveness of coding high-dimensional visual features. By extending the hard assignment to soft assignment, we make it
feasible to incorporate the product quantization as a layer of a convolutional neural network and propose our product quantization network.
Meanwhile, we come up with a novel asymmetric triplet loss, which effectively boosts the retrieval accuracy of the proposed product quantization
network based on asymmetric similarity. Through the proposed product
quantization network, we can obtain a discriminative and compact image representation in an end-to-end manner, which further enables a fast
and accurate image retrieval. Comprehensive experiments conducted on
public benchmark datasets demonstrate the state-of-the-art performance
of the proposed product quantization network