Committees of deep feedforward networks trained with few data

Bogdan Miclut; Thomas Kaester; Thomas Martinetz; Erhardt Barth

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arXiv · 1406.5947

Committees of deep feedforward networks trained with few data

Bogdan Miclut·Thomas Kaester·Thomas Martinetz·Erhardt Barth

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Abstract

Deep convolutional neural networks are known to give good results on image classification tasks. In this paper we present a method to improve the classification result by combining multiple such networks in a committee. We adopt the STL-10 dataset which has very few training examples and show that our method can achieve results that are better than the state of the art. The networks are trained layer-wise and no backpropagation is used. We also explore the effects of dataset augmentation by mirroring, rotation, and scaling.

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Bogdan Miclut, Thomas Kaester, Thomas Martinetz, Erhardt Barth. 2014-06-23. Committees of deep feedforward networks trained with few data. https://arxiv.org/abs/1406.5947

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