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Deep neural networks represent the gold standard for image classification.However, they usually need large amounts of data to reach superior performance.In this work, we focus on image classification problems with a few labeledexamples per class and improve data efficiency by using an ensemble ofrelatively small networks. For the first time, our work broadly studies theexisting concept of neural ensembling in domains with small data, throughextensive validation using popular datasets and architectures. We compareensembles of networks to their deeper or wider single competitors given a totalfixed computational budget. We show that ensembling relatively shallow networksis a simple yet effective technique that is generally better than currentstate-of-the-art approaches for learning from small datasets. Finally, wepresent our interpretation according to which neural ensembles are more sampleefficient because they learn simpler functions.

On the Effectiveness of Neural Ensembles for Image Classification with Small Datasets