Image Classification on Small Datasets via Masked Feature Mixing

• URL: http://arxiv.org/abs/2202.11616v1
• Date: Wed, 23 Feb 2022 16:51:22 GMT
• Title: Image Classification on Small Datasets via Masked Feature Mixing
• Authors: Christoph Reinders, Frederik Schubert, Bodo Rosenhahn
• Abstract summary: A 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.
• Score: 22.105356244579745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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