Semantic Preserving Hash Coding Through VAE-GAN

This paper proposes a novel framework for fast image retrieval. The proposed framework combines variational autoencoder with generative adversarial network to generate content preserving images for learning-based hashing. By accepting real image and systhesized image in a pairwise form, a semantic perserving binary mapping model is learned using pairwise ranking loss under an adversarial generative process. Extensive experiments on several benchmark datasets demonstrate that the proposed method shows substantial improvement over the state-of-the-art hashing methods.