Binary Generative Adversarial Networks for Image Retrieval

Jingkuan Song, Tao He, Lianli Gao, Xing Xu, Alan Hanjalic, Heng Tao Shen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

59 Citations (Scopus)

Abstract

The most striking successes in image retrieval using deep hashing have mostly involved discriminative models, which require labels. In this paper, we use binary generative adversarial networks (BGAN) to embed images to binary codes in an unsupervised way. By restricting the input noise variable of generative adversarial networks (GAN) to be binary and conditioned on the features of each input image, BGAN can simultaneously learn a binary representation per image, and generate an image plausibly similar to the original one. In the proposed framework, we address two main problems: 1) how to directly generate binary codes without relaxation? 2) how to equip the binary representation with the ability of accurate image retrieval? We resolve these problems by proposing new sign-activation strategy and a loss function steering the learning process, which consists of new models for adversarial loss, a content loss, and a neighborhood structure loss. Experimental results on standard datasets (CIFAR-10, NUSWIDE, and Flickr) demonstrate that our BGAN significantly outperforms existing hashing methods by up to 107% in terms of mAP (See Table 2).
Original languageEnglish
Title of host publicationThe Thirty-Second AAAI Conference on Artificial Intelligence, The Thirtieth Innovative Applications of Artificial Intelligence Conference, The Eighth AAAI Symposium on Educational Advances in Artificial Intelligence.
Place of PublicationPalo Alto
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages394-401
Number of pages8
ISBN (Print)978-1-57735-800-8
Publication statusPublished - 2018
EventThe 32nd AAAI Conference on Artificial Intelligence (AAAI-18): The Thirtieth Innovative Applications of Artificial Intelligence Conference, The Eighth AAAI Symposium on Educational Advances in Artificial Intelligence - New Orleans, United States
Duration: 2 Feb 20187 Feb 2018
Conference number: 32nd

Conference

ConferenceThe 32nd AAAI Conference on Artificial Intelligence (AAAI-18)
CountryUnited States
CityNew Orleans
Period2/02/187/02/18

Keywords

  • hashing
  • GAN
  • image retrieval

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