{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/binary-generative-adversarial-networks-for","title":"Binary Generative Adversarial Networks for Image Retrieval","arxiv_id":"1708.04150","date":"2017-08-08","proceeding":null,"authors":["Jingkuan Song"],"abstract":"The most striking successes in image retrieval using deep hashing have mostly\ninvolved discriminative models, which require labels. In this paper, we use\nbinary generative adversarial networks (BGAN) to embed images to binary codes\nin an unsupervised way. By restricting the input noise variable of generative\nadversarial networks (GAN) to be binary and conditioned on the features of each\ninput image, BGAN can simultaneously learn a binary representation per image,\nand generate an image plausibly similar to the original one. In the proposed\nframework, we address two main problems: 1) how to directly generate binary\ncodes without relaxation? 2) how to equip the binary representation with the\nability of accurate image retrieval? We resolve these problems by proposing new\nsign-activation strategy and a loss function steering the learning process,\nwhich consists of new models for adversarial loss, a content loss, and a\nneighborhood structure loss. Experimental results on standard datasets\n(CIFAR-10, NUSWIDE, and Flickr) demonstrate that our BGAN significantly\noutperforms existing hashing methods by up to 107\\% in terms of~mAP (See Table\ntab.res.map.comp) Our anonymous code is available at:\nhttps://github.com/htconquer/BGAN.","url_abs":"http://arxiv.org/abs/1708.04150v1","url_pdf":"http://arxiv.org/pdf/1708.04150v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"binary-generative-adversarial-networks-for","repo_url":"https://github.com/htconquer/BGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-hashing","task_name":"Deep Hashing"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.04150","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}