Factorized Variational Autoencoders for Modeling Audience Reactions to Movies

Zhiwei Deng, Rajitha Navarathna, Peter Carr, Stephan Mandt, Yisong Yue, Iain Matthews, Greg Mori; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2577-2586

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.