  Here we propose a novel model family with the objective of learning to
disentangle the factors of variation in data. Our approach is based on the
spike-and-slab restricted Boltzmann machine which we generalize to include
higher-order interactions among multiple latent variables. Seen from a
generative perspective, the multiplicative interactions emulates the entangling
of factors of variation. Inference in the model can be seen as disentangling
these generative factors. Unlike previous attempts at disentangling latent
factors, the proposed model is trained using no supervised information
regarding the latent factors. We apply our model to the task of facial
expression classification.
