  While Semi-supervised learning has gained much attention in computer vision
on image data, yet limited research exists on its applicability in the time
series domain. In this work, we investigate the transferability of
state-of-the-art deep semi-supervised models from image to time series
classification. We discuss the necessary model adaptations, in particular an
appropriate model backbone architecture and the use of tailored data
augmentation strategies. Based on these adaptations, we explore the potential
of deep semi-supervised learning in the context of time series classification
by evaluating our methods on large public time series classification problems
with varying amounts of labelled samples. We perform extensive comparisons
under a decidedly realistic and appropriate evaluation scheme with a unified
reimplementation of all algorithms considered, which is yet lacking in the
field. We find that these transferred semi-supervised models show significant
performance gains over strong supervised, semi-supervised and self-supervised
alternatives, especially for scenarios with very few labelled samples.
