Adversarial Multi-Label Variational Hashing

In this paper, we propose an adversarial multi-label variational hashing (AMVH) method to learn compact binary codes for efficient image retrieval. Unlike most existing deep hashing methods which only learn binary codes from specific real samples, our AMVH learns hash functions from both synthetic and real data which make our model effective for unseen data. Specifically, we design an end-to-end deep hashing framework which consists of a generator network and a discriminator-hashing network by enforcing simultaneous adversarial learning and discriminative binary codes learning to learn compact binary codes. The discriminator-hashing network learns binary codes by optimizing a multi-label discriminative criterion and minimizing the quantization loss between binary codes and real-value codes. The generator network is learned so that latent representations can be sampled in a probabilistic manner and used to generate new synthetic training sample for the discriminator-hashing network. Experimental results on several benchmark datasets show the efficacy of the proposed approach.

Automated summary

This paper proposes an adversarial multi-label variational hashing method to learn compact binary codes for efficient image retrieval. The method learns hash functions from both synthetic and real data, making it effective for unseen data. By simultaneously enforcing adversarial learning, discriminative binary codes learning, and generating synthetic training samples, the method demonstrates efficacy on benchmark datasets.