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Dropout and similar stochastic neural network regularization methods areoften interpreted as implicitly averaging over a large ensemble of models. Wepropose STE (stochastically trained ensemble) layers, which enhance theaveraging properties of such methods by training an ensemble of weight matriceswith stochastic regularization while explicitly averaging outputs. Thisprovides stronger regularization with no additional computational cost at testtime. We show consistent improvement on various image classification tasksusing standard network topologies.

Regularizing Neural Networks by Stochastically Training Layer Ensembles