{"type":"ScholarlyArticle","title":"Classification and Representation Joint Learning via Deep Networks","authors":[{"name":"Ya Li","slug":"ya-li","given":"Ya","family":"Li","affiliation":"† CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems , University of Science and Technology of China , China"},{"name":"Xinmei Tian","slug":"xinmei-tian","given":"Xinmei","family":"Tian","affiliation":"† CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems , University of Science and Technology of China , China"},{"name":"Xu Shen","slug":"xu-shen","given":"Xu","family":"Shen","affiliation":"† CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems , University of Science and Technology of China , China"},{"name":"Dacheng Tao","slug":"dacheng-tao","given":"Dacheng","family":"Tao","affiliation":"‡ UBTECH Sydney Artificial Intelligence Institute , SIT , FEIT , The University of Sydney , Australia"}],"abstract":"Deep learning has been proven to be effective for classification problems. However, the majority of previous works trained classifiers by considering only class label information and ignoring the local information from the spatial distribution of training samples. In this paper, we propose a deep learning framework that considers both class label information and local spatial distribution information between training samples. A two-channel network with shared weights is used to measure the local distribution. The classification performance can be improved with more detailed information provided by the local distribution, particularly when the training samples are insufficient. Additionally, the class label information can help to learn better feature representations compared with other feature learning methods that use only local distribution information between samples. The local distribution constraint between sample pairs can also be viewed as a regularization of the network, which can efficiently prevent the overfitting problem. Extensive experiments are conducted on several benchmark image classification datasets, and the results demonstrate the effectiveness of our proposed method.","doi":"10.24963/ijcai.2017/308","url":"https://ijcai.org/proceedings/2017/308","pdf":"https://ijcai.org/proceedings/2017/0308.pdf","markdown":"https://ijcai.org/proceedings/2017/308.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/308","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":308,"pages":{"first":2215,"last":2221},"keywords":["Machine Learning: Feature Selection/Construction","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Breiman, 2001] Leo Breiman. 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