Title: On the Effectiveness of Deep Ensembles for Small Data Tasks. Abstract: 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 labeled examples per class and improve sample efficiency in the low data regime by using an ensemble of relatively small deep networks. 
For the first time, our work broadly studies the existing concept of neural ensembling in small data domains, through an extensive validation using popular datasets and architectures.
We show that deep ensembling is a simple yet effective technique that outperforms current state-of-the-art approaches for learning from small datasets.
We compare different ensemble configurations to their deeper and wider competitors given a total fixed computational budget and provide empirical evidence of their advantage.
Furthermore, we investigate the effectiveness of different losses and show that their choice should be made considering different factors.