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

Arkabandhu Chowdhury, Mingchao Jiang, Swarat Chaudhuri, Chris Jermaine; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9445-9454

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.