{"type":"ScholarlyArticle","title":"Affine Equivariant Autoencoder","authors":[{"name":"Xifeng Guo","slug":"xifeng-guo","given":"Xifeng","family":"Guo","affiliation":"College of Computer , National University of Defense Technology , Changsha 410073 , China"},{"name":"En Zhu","slug":"en-zhu","given":"En","family":"Zhu","affiliation":"College of Computer , National University of Defense Technology , Changsha 410073 , China"},{"name":"Xinwang Liu","slug":"xinwang-liu","given":"Xinwang","family":"Liu","affiliation":"College of Computer , National University of Defense Technology , Changsha 410073 , China"},{"name":"Jianping Yin","slug":"jianping-yin","given":"Jianping","family":"Yin","affiliation":"Dongguan University of Technology , Dongguan , China"}],"abstract":"Existing deep neural networks mainly focus on learning transformation invariant features. However, it is the equivariant features that are more adequate for general purpose tasks. Unfortunately, few work has been devoted to learning equivariant features. To fill this gap, in this paper, we propose an affine equivariant autoencoder to learn features that are equivariant to the affine transformation in an unsupervised manner. The objective consists of the self-reconstruction of the original example and affine transformed example, and the approximation of the affine transformation function, where the reconstruction makes the encoder a valid feature extractor and the approximation encourages the equivariance. Extensive experiments are conducted to validate the equivariance and discriminative ability of the features learned by our affine equivariant autoencoder.","doi":"10.24963/ijcai.2019/335","url":"https://ijcai.org/proceedings/2019/335","pdf":"https://ijcai.org/proceedings/2019/0335.pdf","markdown":"https://ijcai.org/proceedings/2019/335.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/335","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":335,"pages":{"first":2413,"last":2419},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Clustering","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Bengio et al., 2013] Yoshua Bengio, Aaron C. 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