  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.
