  Deep neural networks (DNNs) have achieved state-of-the-art results on time
series classification (TSC) tasks. In this work, we focus on leveraging DNNs in
the often-encountered practical scenario where access to labeled training data
is difficult, and where DNNs would be prone to overfitting. We leverage recent
advancements in gradient-based meta-learning, and propose an approach to train
a residual neural network with convolutional layers as a meta-learning agent
for few-shot TSC. The network is trained on a diverse set of few-shot tasks
sampled from various domains (e.g. healthcare, activity recognition, etc.) such
that it can solve a target task from another domain using only a small number
of training samples from the target task. Most existing meta-learning
approaches are limited in practice as they assume a fixed number of target
classes across tasks. We overcome this limitation in order to train a common
agent across domains with each domain having different number of target
classes, we utilize a triplet-loss based learning procedure that does not
require any constraints to be enforced on the number of classes for the
few-shot TSC tasks. To the best of our knowledge, we are the first to use
meta-learning based pre-training for TSC. Our approach sets a new benchmark for
few-shot TSC, outperforming several strong baselines on few-shot tasks sampled
from 41 datasets in UCR TSC Archive. We observe that pre-training under the
meta-learning paradigm allows the network to quickly adapt to new unseen tasks
with small number of labeled instances.
