  Deep convolutional neural networks require large amounts of labeled data
samples. For many real-world applications, this is a major limitation which is
commonly treated by augmentation methods. In this work, we address the problem
of learning deep neural networks on small datasets. Our proposed architecture
called ChimeraMix learns a data augmentation by generating compositions of
instances. The generative model encodes images in pairs, combines the features
guided by a mask, and creates new samples. For evaluation, all methods are
trained from scratch without any additional data. Several experiments on
benchmark datasets, e.g. ciFAIR-10, STL-10, and ciFAIR-100, demonstrate the
superior performance of ChimeraMix compared to current state-of-the-art methods
for classification on small datasets.
