AE2-Nets: Autoencoder in Autoencoder Networks
Learning on data represented with multiple views (e.g., multiple types of descriptors or modalities) is a rapidly growing direction in machine learning and computer vision. Although effectiveness achieved, most existing algorithms usually focus on classification or clustering tasks. Differently, in...
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| Vydáno v: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 2572 - 2580 |
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IEEE
01.06.2019
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| ISSN: | 1063-6919 |
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| Abstract | Learning on data represented with multiple views (e.g., multiple types of descriptors or modalities) is a rapidly growing direction in machine learning and computer vision. Although effectiveness achieved, most existing algorithms usually focus on classification or clustering tasks. Differently, in this paper, we focus on unsupervised representation learning and propose a novel framework termed Autoencoder in Autoencoder Networks (AE^2-Nets), which integrates information from heterogeneous sources into an intact representation by the nested autoencoder framework. The proposed method has the following merits: (1) our model jointly performs view-specific representation learning (with the inner autoencoder networks) and multi-view information encoding (with the outer autoencoder networks) in a unified framework; (2) due to the degradation process from the latent representation to each single view, our model flexibly balances the complementarity and consistence among multiple views. The proposed model is efficiently solved by the alternating direction method (ADM), and demonstrates the effectiveness compared with state-of-the-art algorithms. |
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| AbstractList | Learning on data represented with multiple views (e.g., multiple types of descriptors or modalities) is a rapidly growing direction in machine learning and computer vision. Although effectiveness achieved, most existing algorithms usually focus on classification or clustering tasks. Differently, in this paper, we focus on unsupervised representation learning and propose a novel framework termed Autoencoder in Autoencoder Networks (AE^2-Nets), which integrates information from heterogeneous sources into an intact representation by the nested autoencoder framework. The proposed method has the following merits: (1) our model jointly performs view-specific representation learning (with the inner autoencoder networks) and multi-view information encoding (with the outer autoencoder networks) in a unified framework; (2) due to the degradation process from the latent representation to each single view, our model flexibly balances the complementarity and consistence among multiple views. The proposed model is efficiently solved by the alternating direction method (ADM), and demonstrates the effectiveness compared with state-of-the-art algorithms. |
| Author | Liu, Yeqing Fu, Huazhu Zhang, Changqing |
| Author_xml | – sequence: 1 givenname: Changqing surname: Zhang fullname: Zhang, Changqing organization: Tianjin Univ – sequence: 2 givenname: Yeqing surname: Liu fullname: Liu, Yeqing organization: Tianjin Univ – sequence: 3 givenname: Huazhu surname: Fu fullname: Fu, Huazhu organization: Inception Institute of Artificial Intelligence |
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| SubjectTerms | Autoencoders Computer vision Data mining Data models Degradation Encoding Feature extraction Machine learning algorithms Neural networks Representation learning Statistical Learning |
| Title | AE2-Nets: Autoencoder in Autoencoder Networks |
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