  From human physiology to environmental evolution, important processes in
nature often exhibit meaningful and strong periodic or quasi-periodic changes.
Due to their inherent label scarcity, learning useful representations for
periodic tasks with limited or no supervision is of great benefit. Yet,
existing self-supervised learning (SSL) methods overlook the intrinsic
periodicity in data, and fail to learn representations that capture periodic or
frequency attributes. In this paper, we present SimPer, a simple contrastive
SSL regime for learning periodic information in data. To exploit the periodic
inductive bias, SimPer introduces customized augmentations, feature similarity
measures, and a generalized contrastive loss for learning efficient and robust
periodic representations. Extensive experiments on common real-world tasks in
human behavior analysis, environmental sensing, and healthcare domains verify
the superior performance of SimPer compared to state-of-the-art SSL methods,
highlighting its intriguing properties including better data efficiency,
robustness to spurious correlations, and generalization to distribution shifts.
Code and data are available at: https://github.com/YyzHarry/SimPer.
