  In this paper, we introduce a new architecture for few shot learning, the
task of teaching a neural network from as few as one or five labeled examples.
Inspired by the theoretical results of Alaine et al that Denoising Autoencoders
refine features to lie closer to the true data manifold, we present a new
training scheme that adds noise at multiple stages of an existing neural
architecture while simultaneously learning to be robust to this added noise.
This architecture, which we call a Self-Denoising Neural Network (SDNN), can be
applied easily to most modern convolutional neural architectures, and can be
used as a supplement to many existing few-shot learning techniques. We
empirically show that SDNNs out-perform previous state-of-the-art methods for
few shot image recognition using the Wide-ResNet architecture on the
\textit{mini}ImageNet, tiered-ImageNet, and CIFAR-FS few shot learning
datasets. We also perform a series of ablation experiments to empirically
justify the construction of the SDNN architecture. Finally, we show that SDNNs
even improve few shot performance on the task of human action detection in
video using experiments on the ActEV SDL Surprise Activities challenge.
