Papers › SimPer: Simple Self-Supervised Learning of Periodic Targets

SimPer: Simple Self-Supervised Learning of Periodic Targets

6 Oct 2022arXiv:2210.03115archive 2025-07-28

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

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.

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Classifier yyzharry/simper/src/simper.py official repository ran · our draft was wrong licence not identified · pointer only · aeb749b212a5e4b5 · report
Featurizer yyzharry/simper/src/simper.py official repository ran licence not identified · pointer only · 4ac0d2e193eab8a6 · report
label_distance yyzharry/simper/src/simper.py official repository ran · fixture could not drive it licence not identified · pointer only · f363c4aadd37b305 · report
SimPer yyzharry/simper/src/simper.py official repository unverified licence not identified · pointer only · 337a79c4eeb2fae4 · report

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Inductive BiasSelf-Supervised Learning

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