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Regularisation of deep neural networks (DNN) during training is critical toperformance. By far the most popular method is known as dropout. Here, castthrough the prism of signal processing theory, we compare and contrast theregularisation effects of dropout with those of dither. We illustrate someserious inherent limitations of dropout and demonstrate that dither provides amore effective regulariser.

Dither is Better than Dropout for Regularising Deep Neural Networks