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arXiv · 2202.11616

ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing

Christoph Reinders·Frederik Schubert·Bodo Rosenhahn
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Abstract

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.

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Christoph Reinders, Frederik Schubert, Bodo Rosenhahn. 2022-02-23. ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing. https://arxiv.org/abs/2202.11616

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