Committees of deep feedforward networks trained with few data

Bogdan Miclut; Thomas Kaester; Thomas Martinetz; Erhardt Barth

Committees of deep feedforward networks trained with few data

Bogdan Miclut, Thomas Kaester, Thomas Martinetz, Erhardt Barth

Institute of Neuro- and Bioinformatics

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.

Original language	English
Journal	arXiv.org
Number of pages	7
Publication status	Published - 23.06.2014

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1406.5947v1Final published version, 112 KB

https://arxiv.org/abs/1406.5947

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title = "Committees of deep feedforward networks trained with few data",

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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TY - JOUR

T1 - Committees of deep feedforward networks trained with few data

AU - Miclut, Bogdan

AU - Kaester, Thomas

AU - Martinetz, Thomas

AU - Barth, Erhardt

PY - 2014/6/23

Y1 - 2014/6/23

N2 - 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.

AB - 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.

M3 - Journal articles

JO - arXiv.org

JF - arXiv.org

ER -

Committees of deep feedforward networks trained with few data

Abstract

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