  These days deep learning is the fastest-growing area in the field of Machine
Learning. Convolutional Neural Networks are currently the main tool used for
image analysis and classification purposes. Although great achievements and
perspectives, deep neural networks and accompanying learning algorithms have
some relevant challenges to tackle. In this paper, we have focused on the most
frequently mentioned problem in the field of machine learning, that is
relatively poor generalization abilities. Partial remedies for this are
regularization techniques e.g. dropout, batch normalization, weight decay,
transfer learning, early stopping and data augmentation. In this paper, we have
focused on data augmentation. We propose to use a method based on a neural
style transfer, which allows generating new unlabeled images of a high
perceptual quality that combine the content of a base image with the appearance
of another one. In a proposed approach, the newly created images are described
with pseudo-labels, and then used as a training dataset. Real, labeled images
are divided into the validation and test set. We validated the proposed method
on a challenging skin lesion classification case study. Four representative
neural architectures are examined. Obtained results show the strong potential
of the proposed approach.
