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arXiv · 1508.07130

Parallel Dither and Dropout for Regularising Deep Neural Networks

Andrew J. R. Simpson
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Abstract

Effective regularisation during training can mean the difference between success and failure for deep neural networks. Recently, dither has been suggested as alternative to dropout for regularisation during batch-averaged stochastic gradient descent (SGD). In this article, we show that these methods fail without batch averaging and we introduce a new, parallel regularisation method that may be used without batch averaging. Our results for parallel-regularised non-batch-SGD are substantially better than what is possible with batch-SGD. Furthermore, our results demonstrate that dither and dropout are complimentary.

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BibTeXRIS

Andrew J. R. Simpson. 2015-08-28. Parallel Dither and Dropout for Regularising Deep Neural Networks. https://arxiv.org/abs/1508.07130

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