Factorized Variational Autoencoders for Modeling Audience Reactions to Movies
Zhiwei Deng, Rajitha Navarathna, Peter Carr, Stephan Mandt, Yisong Yue, Iain Matthews, Greg Mori
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 factoriza- tion 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 ap- proach to a large dataset of facial expressions of movie- watching audiences (over 16 million faces). Our experi- ments show that compared to conventional linear factoriza- tion methods, our method achieves better reconstruction of the data, and further discovers interpretable latent factors.
BibTeX
@inproceedings{cvpr2017_factorizedvariat,
title = {Factorized Variational Autoencoders for Modeling Audience Reactions to Movies},
author = {Zhiwei Deng and Rajitha Navarathna and Peter Carr and Stephan Mandt and Yisong Yue and Iain Matthews and Greg Mori},
booktitle = {CVPR 2017},
year = {2017}
}