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Deep convolutional neural networks require large amounts of labeled datasamples. For many real-world applications, this is a major limitation which iscommonly treated by augmentation methods. In this work, we address the problemof learning deep neural networks on small datasets. Our proposed architecturecalled ChimeraMix learns a data augmentation by generating compositions ofinstances. The generative model encodes images in pairs, combines the featuresguided by a mask, and creates new samples. For evaluation, all methods aretrained from scratch without any additional data. Several experiments onbenchmark datasets, e.g. ciFAIR-10, STL-10, and ciFAIR-100, demonstrate thesuperior performance of ChimeraMix compared to current state-of-the-art methodsfor classification on small datasets.

ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing