Dither is Better than Dropout for Regularising Deep Neural Networks

Andrew J. R. Simpson

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arXiv · 1508.04826

Dither is Better than Dropout for Regularising Deep Neural Networks

Andrew J. R. Simpson

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Abstract

Regularisation of deep neural networks (DNN) during training is critical to performance. By far the most popular method is known as dropout. Here, cast through the prism of signal processing theory, we compare and contrast the regularisation effects of dropout with those of dither. We illustrate some serious inherent limitations of dropout and demonstrate that dither provides a more effective regulariser.

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Andrew J. R. Simpson. 2015-08-26. Dither is Better than Dropout for Regularising Deep Neural Networks. https://arxiv.org/abs/1508.04826

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