{"type":"ScholarlyArticle","title":"Graph and Autoencoder Based Feature Extraction for Zero-shot Learning","authors":[{"name":"Yang Liu","slug":"yang-liu","given":"Yang","family":"Liu","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an 710071 , China"},{"name":"Deyan Xie","slug":"deyan-xie","given":"Deyan","family":"Xie","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an 710071 , China"},{"name":"Quanxue Gao","slug":"quanxue-gao","given":"Quanxue","family":"Gao","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an 710071 , China"},{"name":"Jungong Han","slug":"jungong-han","given":"Jungong","family":"Han","affiliation":"WMG Data Science , University of Warwick , CV4 7AL Coventry , United Kingdom"},{"name":"Shujian Wang","slug":"shujian-wang","given":"Shujian","family":"Wang","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an 710071 , China"},{"name":"Xinbo Gao","slug":"xinbo-gao","given":"Xinbo","family":"Gao","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an 710071 , China; School of Electronic Engineering , Xidian University , Xi'an 710071 , China"}],"abstract":"Zero-shot learning (ZSL) aims to build models to recognize novel visual categories that have no associated labelled training samples. The basic framework is to transfer knowledge from seen classes to unseen classes by learning the visual-semantic embedding. However, most of approaches do not preserve the underlying sub-manifold of samples in the embedding space. In addition, whether the mapping can precisely reconstruct the original visual feature is not investigated in-depth. In order to solve these problems, we formulate a novel framework named Graph and Autoencoder Based Feature Extraction (GAFE) to seek a low-rank mapping to preserve the sub-manifold of samples. Taking the encoder-decoder paradigm, the encoder part learns a mapping from the visual feature to the semantic space, while decoder part reconstructs the original features with the learned mapping. In addition, a graph is constructed to guarantee the learned mapping can preserve the local intrinsic structure of the data. To this end, an L21 norm sparsity constraint is imposed on the mapping to identify features relevant to the target domain. Extensive experiments on five attribute datasets demonstrate the effectiveness of the proposed model.","doi":"10.24963/ijcai.2019/421","url":"https://ijcai.org/proceedings/2019/421","pdf":"https://ijcai.org/proceedings/2019/0421.pdf","markdown":"https://ijcai.org/proceedings/2019/421.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/421","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":421,"pages":{"first":3038,"last":3044},"keywords":["Machine Learning: Dimensionality Reduction and Manifold Learning","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Akata et al., 2015] Zeynep Akata, Scott E Reed, Daniel J Walter, Honglak Lee, and Bernt Schiele. 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