Learning to Disentangle Factors of Variation with Manifold Interaction
Scott Reed, Kihyuk Sohn, Yuting Zhang, Honglak Lee · 2014
Many latent factors of variation interact to gen-erate sensory data; for example, pose, morphol-ogy and expression in face images. In this work, we propose to learn manifold coordinates for the relevant factors of variation and to model their joint interaction. Many existing feature learning algorithms focus on a single task and extract fea-tures that are sensitive to the task-relevant factors and invariant to all others. However, models that just extract a single set of invariant features do not exploit the relationships among the latent fac-tors. To address this, we propose a higher-order Boltzmann machine that incorporates multiplica-tive interactions among groups of hidden units that each learn to encode a distinct factor of vari-ation. Furthermore, we propose correspondence-based training strategies that allow effective dis-entangling. Our model achieves state-of-the-art emotion recognition and face verification perfor-mance on the Toronto Face Database. We also demonstrate disentangled features learned on the CMU Multi-PIE dataset. 1.