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

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arxiv 1911.04623 v2 pith:ER37RR3N submitted 2019-11-12 cs.CV

Yan Wang , Wei-Lun Chao , Kilian Q. Weinberger , Laurens van der Maaten This is my paper · ORCID
classification cs.CV
keywords few-shotnearest-neighborclassificationclassifierfindlearnerslearningmeta-learning
verification ladder T0 review T1 audit T2 compute T3 formal
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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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