{"type":"ScholarlyArticle","title":"Progressive Generative Hashing for Image Retrieval","authors":[{"name":"Yuqing Ma","slug":"yuqing-ma","given":"Yuqing","family":"Ma","affiliation":"State Key Lab of Software Development Environment , Beihang University , China"},{"name":"Yue He","slug":"yue-he","given":"Yue","family":"He","affiliation":"State Key Lab of Software Development Environment , Beihang University , China"},{"name":"Fan Ding","slug":"fan-ding","given":"Fan","family":"Ding","affiliation":"State Key Lab of Software Development Environment , Beihang University , China"},{"name":"Sheng Hu","slug":"sheng-hu","given":"Sheng","family":"Hu","affiliation":"State Key Lab of Software Development Environment , Beihang University , China"},{"name":"Jun Li","slug":"jun-li","given":"Jun","family":"Li","affiliation":"State Key Lab of Software Development Environment , Beihang University , China"},{"name":"Xianglong Liu","slug":"xianglong-liu","given":"Xianglong","family":"Liu","affiliation":"State Key Lab of Software Development Environment , Beihang University , China"}],"abstract":"Recent years have witnessed the success of the emerging hashing techniques in large-scale image retrieval. Owing to the great learning capacity, deep hashing has become one of the most promising solutions, and achieved attractive performance in practice. However, without semantic label information, the unsupervised deep hashing still remains an open question. In this paper, we propose a novel progressive generative hashing (PGH) framework to help learn a discriminative hashing network in an unsupervised way. Very different from existing studies, it first treats the hash codes as a kind of semantic condition for the similar image generation, and simultaneously feeds the original image and its codes into the generative adversarial networks (GANs). The real images together with the synthetic ones can further help train a discriminative hashing network based on a triplet loss. By iteratively inputting the learnt codes into the hash conditioned GANs, we can progressively enable the hashing network to discover the semantic relations. Extensive experiments on the widely-used image datasets demonstrate that PGH can significantly outperforms state-of-the-art unsupervised hashing methods.","doi":"10.24963/ijcai.2018/121","url":"https://ijcai.org/proceedings/2018/121","pdf":"https://ijcai.org/proceedings/2018/0121.pdf","markdown":"https://ijcai.org/proceedings/2018/121.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/121","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Computer Vision","track":"Main track","number":121,"pages":{"first":871,"last":877},"keywords":["Machine Learning: Unsupervised Learning","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation","Computer Vision: Computer Vision"],"videos":[],"references":[{"position":1,"raw":"[Datar et al., 2004] Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S. 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