Few-shot learning for deep neural networks is a highly challenging and key problem in 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 fewshot learning, and (ii) implanting, that is, attaching new neurons to a previously trained network to learn new, taskspecific features. Implanting enables training of multiple layers in the few-shot regime, departing from most related methods derived from metric learning that train only the final layer. Both contributions show consistent gains when used individually or jointly and we report state of the art performance on few-shot classification on miniImageNet. 1 We use the notation [i] := {1, . . . , i} for i ∈ N.
Meanwhile, Lifchitz et al. REF proposed dense classification over feature maps to take full advantage of the local activations and spatial information.
Dense Classification and Implanting for Few-Shot Learning
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science