  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.
