Deng, Zhiwei and Navarathna, Rajitha and Carr, Peter and Mandt, Stephan and Yue, Yisong and Matthews, Iain and Mori, Greg (2017) Factorized Variational Autoencoders for Modeling Audience Reactions to Movies. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE , Piscataway, NJ. ISBN 978-1-5386-0457-1. https://resolver.caltech.edu/CaltechAUTHORS:20170721-141656092
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Abstract
Matrix and tensor factorization methods are often used for finding underlying low-dimensional patterns from noisy data. In this paper, we study non-linear tensor factorization methods based on deep variational autoencoders. Our approach is well-suited for settings where the relationship between the latent representation to be learned and the raw data representation is highly complex. We apply our approach to a large dataset of facial expressions of movie-watching audiences (over 16 million faces). Our experiments show that compared to conventional linear factorization methods, our method achieves better reconstruction of the data, and further discovers interpretable latent factors.
Item Type: | Book Section | ||||||||||||
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Additional Information: | © 2017 IEEE. | ||||||||||||
DOI: | 10.1109/CVPR.2017.637 | ||||||||||||
Record Number: | CaltechAUTHORS:20170721-141656092 | ||||||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20170721-141656092 | ||||||||||||
Official Citation: | Z. Deng et al., "Factorized Variational Autoencoders for Modeling Audience Reactions to Movies," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 6014-6023. doi: 10.1109/CVPR.2017.637. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8100120&isnumber=8099483 | ||||||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||
ID Code: | 79271 | ||||||||||||
Collection: | CaltechAUTHORS | ||||||||||||
Deposited By: | Tony Diaz | ||||||||||||
Deposited On: | 21 Jul 2017 21:57 | ||||||||||||
Last Modified: | 15 Nov 2021 17:46 |
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