  Accurate image classification given small amounts of labelled data (few-shot
classification) remains an open problem in computer vision. In this work we
examine how the known texture bias of Convolutional Neural Networks (CNNs)
affects few-shot classification performance. Although texture bias can help in
standard image classification, in this work we show it significantly harms
few-shot classification performance. After correcting this bias we demonstrate
state-of-the-art performance on the competitive miniImageNet task using a
method far simpler than the current best performing few-shot learning
approaches.
