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Disentangling Factors of Variation via Generative Entangling
TLDR
This work proposes a novel model family based on the spike-and-slab restricted Boltzmann machine which is generalize to include higher-order interactions among multiple latent variables and applies it to the task of facial expression classification.Abstract:
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.read more
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