{"type":"ScholarlyArticle","title":"SitNet: Discrete Similarity Transfer Network for Zero-shot Hashing","authors":[{"name":"Yuchen Guo","slug":"yuchen-guo","given":"Yuchen","family":"Guo","affiliation":"† School of Software , Tsinghua University , Beijing 100084 , China"},{"name":"Guiguang Ding","slug":"guiguang-ding","given":"Guiguang","family":"Ding","affiliation":"† School of Software , Tsinghua University , Beijing 100084 , China"},{"name":"Jungong Han","slug":"jungong-han","given":"Jungong","family":"Han","affiliation":"‡ School of Computing & Communications , Lancaster University , UK"},{"name":"Yue Gao","slug":"yue-gao","given":"Yue","family":"Gao","affiliation":"† School of Software , Tsinghua University , Beijing 100084 , China"}],"abstract":"Hashing has been widely utilized for fast image retrieval recently. With semantic information as supervision, hashing approaches perform much better, especially when combined with deep convolution neural network(CNN). However, in practice, new concepts emerge every day, making collecting supervised information for re-training hashing model infeasible. In this paper, we propose a novel zero-shot hashing approach, called Discrete Similarity Transfer Network (SitNet), to preserve the semantic similarity between images from both ``seen'' concepts and new ``unseen'' concepts. Motivated by zero-shot learning, the semantic vectors of concepts are adopted to capture the similarity structures among classes, making the model trained with seen concepts generalize well for unseen ones benefiting from the transferability of the semantic vector space. We adopt a multi-task architecture to exploit the supervised information for seen concepts and the semantic vectors simultaneously. Moreover, a discrete hashing layer is integrated into the network for hashcode generating to avoid the information loss caused by real-value relaxation in training phase, which is a critical problem in existing works. Experiments on three benchmarks validate the superiority of SitNet to the state-of-the-arts.","doi":"10.24963/ijcai.2017/245","url":"https://ijcai.org/proceedings/2017/245","pdf":"https://ijcai.org/proceedings/2017/0245.pdf","markdown":"https://ijcai.org/proceedings/2017/245.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/245","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":245,"pages":{"first":1767,"last":1773},"keywords":["Machine Learning: Feature Selection/Construction","Machine Learning: Data Mining","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Akata et al., 2016] Zeynep Akata, Mateusz Malinowski, Mario Fritz, and Bernt Schiele. 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