SimPer: Simple Self-Supervised Learning of Periodic Targets

Yuzhe Yang, Xin Liu, Jiang Wu, Silviu Borac, Dina Katabi, Ming-Zher Poh, Daniel McDuff

Keywords: Periodic learning, Self-supervised learning, Representation learning, Periodic targets, Periodicity

TL;DR: A simple contrastive self-supervised framework for learning periodic targets and tasks.

Abstract: 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.

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Please Choose The Closest Area That Your Submission Falls Into: Unsupervised and Self-supervised learning

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/simper-simple-self-supervised-learning-of/code)