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arXiv · 1910.08519

Texture Bias Of CNNs Limits Few-Shot Classification Performance

Sam Ringer·Will Williams·Tom Ash·Remi Francis·David MacLeod
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Abstract

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.

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BibTeXRIS

Sam Ringer, Will Williams, Tom Ash, Remi Francis, David MacLeod. 2019-10-18. Texture Bias Of CNNs Limits Few-Shot Classification Performance. https://arxiv.org/abs/1910.08519

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