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Regularization is essential when training large neural networks. As deepneural networks can be mathematically interpreted as universal functionapproximators, they are effective at memorizing sampling noise in the trainingdata. This results in poor generalization to unseen data. Therefore, it is nosurprise that a new regularization technique, Dropout, was partiallyresponsible for the now-ubiquitous winning entry to ImageNet 2012 by theUniversity of Toronto. Currently, Dropout (and related methods such asDropConnect) are the most effective means of regularizing large neuralnetworks. These amount to efficiently visiting a large number of related modelsat training time, while aggregating them to a single predictor at test time.The proposed FaMe model aims to apply a similar strategy, yet learns afactorization of each weight matrix such that the factors are robust to noise.

Neural Network Regularization via Robust Weight Factorization