Dropout has been proven to be an effective method for reducing overfitting in deep artificial neural networks. We present 3 new alternative methods for performing dropout on a deep neural network which improves the effectiveness of the dropout method over the same training period. These methods select neurons to be dropped through statistical values calculated using a neurons change in weight, the average size of a neuron’s weights, and the output variance of a neuron. We found that increasing the probability of dropping neurons with smaller values of these statistics and decreasing the probability of those with larger statistics gave an improved result in training over 10, 000 epochs. The most effective of these was found to be the Output Variance method, giving an average improvement of 1.17% accuracy over traditional dropout methods.
|Title of host publication||Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings|
|Editors||Akira Hirose, Minho Lee, Derong Liu, Kenji Doya, Kazushi Ikeda, Seiichi Ozawa|
|Number of pages||10|
|Publication status||Published - 2016|
|Event||13th International Conference on Neural Information Processing Systems - Barcelona, Spain|
Duration: 5 Dec 2016 → 10 Dec 2016
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||13th International Conference on Neural Information Processing Systems|
|Period||5/12/16 → 10/12/16|
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© Springer International Publishing AG 2016.
Copyright 2017 Elsevier B.V., All rights reserved.