{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/few-shot-image-classification-just-use-a","title":"Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier","arxiv_id":"2101.00562","date":"2021-01-03","proceeding":"ICCV 2021 10","authors":["Arkabandhu Chowdhury","Mingchao Jiang","Swarat Chaudhuri","Chris Jermaine"],"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. 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