  Label-efficient time series representation learning, which aims to learn
effective representations with limited labeled data, is crucial for deploying
deep learning models in real-world applications. To address the scarcity of
labeled time series data, various strategies, e.g., transfer learning,
self-supervised learning, and semi-supervised learning, have been developed. In
this survey, we introduce a novel taxonomy for the first time, categorizing
existing approaches as in-domain or cross-domain, based on their reliance on
external data sources or not. Furthermore, we present a review of the recent
advances in each strategy, conclude the limitations of current methodologies,
and suggest future research directions that promise further improvements in the
field.
