  Training deep neural networks from few examples is a highly challenging and
key problem for many computer vision tasks. In this context, we are targeting
knowledge transfer from a set with abundant data to other sets with few
available examples. We propose two simple and effective solutions: (i) dense
classification over feature maps, which for the first time studies local
activations in the domain of few-shot learning, and (ii) implanting, that is,
attaching new neurons to a previously trained network to learn new,
task-specific features. On miniImageNet, we improve the prior state-of-the-art
on few-shot classification, i.e., we achieve 62.5%, 79.8% and 83.8% on 5-way
1-shot, 5-shot and 10-shot settings respectively.
