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

• URL: http://arxiv.org/abs/2101.00562v1
• Date: Sun, 3 Jan 2021 05:30:36 GMT
• Title: Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier
• Authors: Arkabandhu Chowdhury, Mingchao Jiang, Chris Jermaine
• Abstract summary: We show 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.
• Score: 5.782827425991282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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