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Larger networks generally have greater representational power at the cost ofincreased computational complexity. Sparsifying such networks has been anactive area of research but has been generally limited to static regularizationor dynamic approaches using reinforcement learning. We explore a mixture ofexperts (MoE) approach to deep dynamic routing, which activates certain expertsin the network on a per-example basis. Our novel DeepMoE architecture increasesthe representational power of standard convolutional networks by adaptivelysparsifying and recalibrating channel-wise features in each convolutionallayer. We employ a multi-headed sparse gating network to determine theselection and scaling of channels for each input, leveraging exponentialcombinations of experts within a single convolutional network. Our proposedarchitecture is evaluated on four benchmark datasets and tasks, and we showthat Deep-MoEs are able to achieve higher accuracy with lower computation thanstandard convolutional networks.

Deep Mixture of Experts via Shallow Embedding