Modout: Learning Multi-modal Architectures by Stochastic Regularization

IEEE Conference on Automatic Face and Gesture Recognition (FG 2017)


Model selection methods based on stochastic regularization have been widely used in deep learning due to their simplicity and effectiveness. The well-known Dropout method treats all units, visible or hidden, in the same way, thus ignoring any a priori information related to grouping or structure. Such structure is present in multi-modal learning applications such as affect analysis and gesture recognition, where subsets of units may correspond to individual modalities. Here we describe Modout, a model selection method based on stochastic regularization, which is particularly useful in the multi-modal setting. Different from other forms of stochastic regularization, it is capable of learning whether or when to fuse two modalities in a layer, which is usually considered to be an architectural hyper-parameter by deep learning researchers and practitioners. Modout is evaluated on two real multi-modal datasets. The results indicate improved performance compared to other forms of stochastic regularization. The result on the Montalbano dataset shows that learning a fusion structure by Modout is on par with a state-of-the-art carefully designed architecture.

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