# Texture Bias Of CNNs Limits Few-Shot Classification Performance

arXiv: https://arxiv.org/abs/1910.08519 · HTML: https://mlsanity.com/paper/1910.08519/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2411.01432/) (2024)
- [Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation](https://mlsanity.com/paper/2308.07251/) (2023)
- [LSFSL: Leveraging Shape Information in Few-shot Learning](https://mlsanity.com/paper/2304.06672/) (2023)

## Abstract (arXiv)

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.