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.
@article{arxiv.2202.11616,
title = {ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing},
author = {Christoph Reinders and Frederik Schubert and Bodo Rosenhahn},
journal= {arXiv preprint arXiv:2202.11616},
year = {2022}
}