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Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier

3 Jan 2021ICCV 2021 10arXiv:2101.00562archive 2025-07-28

Arkabandhu Chowdhury, Mingchao Jiang, Swarat Chaudhuri, Chris Jermaine

Recent papers have suggested that transfer learning can outperform sophisticated meta-learning methods for few-shot image classification. We take this hypothesis to its logical conclusion, and suggest the use of an ensemble of high-quality, pre-trained feature extractors for few-shot image classification. We show experimentally that a library of pre-trained feature extractors combined with a simple feed-forward network learned with an L2-regularizer can be an excellent option for solving cross-domain few-shot image classification. Our experimental results suggest that this simpler sample-efficient approach far outperforms several well-established meta-learning algorithms on a variety of few-shot tasks.

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