  The deep Convolutional Neural Network (CNN) became very popular as a
fundamental technique for image classification and objects recognition. To
improve the recognition accuracy for the more complex tasks, deeper networks
have being introduced. However, the recognition accuracy of the trained deep
CNN drastically decreases for the samples which are obtained from the outside
regions of the training samples. To improve the generalization ability for such
samples, Krizhevsky et al. proposed to generate additional samples through
transformations from the existing samples and to make the training samples
richer. This method is known as data augmentation. Hongyi Zhang et al.
introduced data augmentation method called mixup which achieves
state-of-the-art performance in various datasets. Mixup generates new samples
by mixing two different training samples. Mixing of the two images is
implemented with simple image morphing. In this paper, we propose to apply
mixup to the feature maps in a hidden layer. To implement the mixup in the
hidden layer we use the Siamese network or the triplet network architecture to
mix feature maps. From the experimental comparison, it is observed that the
mixup of the feature maps obtained from the first convolution layer is more
effective than the original image mixup.
