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arXiv · 1911.04623

SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning

Yan Wang·Wei-Lun Chao·Kilian Q. Weinberger·Laurens van der Maaten
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Abstract

Few-shot learners aim to recognize new object classes based on a small number of labeled training examples. To prevent overfitting, state-of-the-art few-shot learners use meta-learning on convolutional-network features and perform classification using a nearest-neighbor classifier. This paper studies the accuracy of nearest-neighbor baselines without meta-learning. Surprisingly, we find simple feature transformations suffice to obtain competitive few-shot learning accuracies. For example, we find that a nearest-neighbor classifier used in combination with mean-subtraction and L2-normalization outperforms prior results in three out of five settings on the miniImageNet dataset.

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Yan Wang, Wei-Lun Chao, Kilian Q. Weinberger, Laurens van der Maaten. 2019-11-12. SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning. https://arxiv.org/abs/1911.04623

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