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.
@article{arxiv.1903.05050,
title = {Dense Classification and Implanting for Few-Shot Learning},
author = {Yann Lifchitz and Yannis Avrithis and Sylvaine Picard and Andrei Bursuc},
journal= {arXiv preprint arXiv:1903.05050},
year = {2019}
}