{"type":"ScholarlyArticle","title":"One Network for Multi-Domains: Domain Adaptive Hashing with Intersectant Generative Adversarial Networks","authors":[{"name":"Tao He","slug":"tao-he","given":"Tao","family":"He","affiliation":"Center for Future Media and School of Computer Science and Engineering , University of Electronic Science and Technology of China , Chengdu , China; Faculty of Information Technology , Monash University , Clayton , Australia"},{"name":"Yuan-Fang Li","slug":"yuan-fang-li","given":"Yuan-Fang","family":"Li","affiliation":"Faculty of Information Technology , Monash University , Clayton , Australia"},{"name":"Lianli Gao","slug":"lianli-gao","given":"Lianli","family":"Gao","affiliation":"Center for Future Media and School of Computer Science and Engineering , University of Electronic Science and Technology of China , Chengdu , China"},{"name":"Dongxiang Zhang","slug":"dongxiang-zhang","given":"Dongxiang","family":"Zhang","affiliation":"Center for Future Media and School of Computer Science and Engineering , University of Electronic Science and Technology of China , Chengdu , China"},{"name":"Jingkuan Song","slug":"jingkuan-song","given":"Jingkuan","family":"Song","affiliation":"Center for Future Media and School of Computer Science and Engineering , University of Electronic Science and Technology of China , Chengdu , China"}],"abstract":"With the recent explosive increase of digital data, image recognition and retrieval become a critical practical application. Hashing is an effective solution to this problem, due to its low storage requirement and high query speed. However, most of past works focus on hashing in a single (source) domain. Thus, the learned hash function may not adapt well in a new (target) domain that has a large distributional difference with the source domain. In this paper, we explore an end-to-end domain adaptive learning framework that simultaneously and precisely generates discriminative hash codes and classifies target domain images. Our method encodes two domains images into a semantic common space, followed by two independent generative adversarial networks arming at crosswise reconstructing two domains’ images, reducing domain disparity and improving alignment in the shared space. We evaluate our framework on four public benchmark datasets, all of which show that our method is superior to the other state-of-the-art methods on the tasks of object recognition and image retrieval.","doi":"10.24963/ijcai.2019/344","url":"https://ijcai.org/proceedings/2019/344","pdf":"https://ijcai.org/proceedings/2019/0344.pdf","markdown":"https://ijcai.org/proceedings/2019/344.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/344","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":344,"pages":{"first":2477,"last":2483},"keywords":["Multidisciplinary Topics and Applications: Information Retrieval","Machine Learning: Transfer, Adaptation, Multi-task Learning"],"videos":[],"references":[{"position":1,"raw":"[Carreira-Perpiñán and Raziperchikolaei, 2015] Miguel Á. 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