Dense Classification and Implanting for Few-Shot Learning
This addresses the problem of training deep neural networks with limited data for computer vision tasks, offering incremental improvements over prior methods.
The paper tackles few-shot learning by proposing dense classification over feature maps and implanting new neurons, achieving state-of-the-art results on miniImageNet with 62.5% (1-shot), 79.8% (5-shot), and 83.8% (10-shot) accuracy.
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.