SimPer: Simple Self-Supervised Learning of Periodic Targets (2210.03115v2)

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. Code and data are available at: https://github.com/YyzHarry/SimPer.

• The paper introduces SimPer, a self-supervised framework for learning periodic targets by exploiting temporal self-contrastive learning and periodicity-aware similarity measures.
• SimPer consistently outperforms state-of-the-art self-supervised methods on diverse periodic datasets, demonstrating superior frequency resolution and generalization.
• This simple and versatile method offers a promising direction for self-supervised learning in domains with structured periodic data where obtaining labels is difficult.

Overview of "SimPer: Simple Self-Supervised Learning of Periodic Targets"

This paper introduces SimPer, a method for self-supervised learning (SSL) that focuses on learning periodic information from data. The authors argue that current SSL methods overlook the inherent periodicity in datasets, thereby limiting their effectiveness in capturing frequency and periodic attributes crucial for a range of applications such as human physiological monitoring, environmental sensing, and analysis of human behavior. SimPer is designed to address these limitations by proposing a novel self-supervised framework specifically for periodic learning tasks.

Key Contributions

1. Temporal Self-Contrastive Learning Framework: The authors present a unique approach to building self-supervised tasks that directly exploit periodic inductive biases. This involves the creation of periodicity-variant and periodicity-invariant augmentations of the input data. By contrasting these views, SimPer effectively leverages the essential characteristics of periodic data, providing a robust and efficient way to learn useful representations.
2. Periodic Feature Similarity Measures: To handle the intrinsic periodic nature of the task, SimPer introduces specific feature similarity measures. These include maximum cross-correlation and normalized power spectrum density, which are adept at capturing periodic information compared to traditional metrics like cosine similarity.
3. Generalized Contrastive Loss: The paper extends the classic InfoNCE loss to accommodate continuous label spaces found in periodic tasks. This generalized loss integrates similarity measures in the frequency domain, thus enabling better handling of continuous targets.

Experimental Results

SimPer is evaluated across six diverse datasets related to human physiology, action counting, and environmental sensing. The empirical results show that SimPer consistently outperforms state-of-the-art SSL methods on these datasets. Notably, experiments demonstrate SimPer's superior ability to maintain high frequency resolution and effective generalization, even with reduced training data or unseen targets. Furthermore, the results highlight the robustness of SimPer against spurious correlations, showcasing its potential for domain adaptation and transfer learning scenarios.

Discussion on Implications and Future Work

The implications of SimPer extend to both practical applications and theoretical advancements. Practically, SimPer can significantly lower the barrier for learning periodic information from data where labels are scarce or expensive to obtain. This is particularly valuable in fields like healthcare, where obtaining labeled examples is often challenging due to privacy concerns and the need for specialized equipment.

Theoretically, the introduction of periodic similarity measures and a generalized contrastive loss opens up new avenues for research in SSL. These innovations could be applied beyond periodic learning tasks to other domains where data has continuous and structured relationships.

For future work, exploring the integration of domain-specific knowledge into SimPer could further enhance its performance. Additionally, examining the application of SimPer in other periodic datasets outside the domains discussed could provide deeper insights into its versatility and efficiency.

Conclusion

SimPer contributes a significant advancement in the area of SSL for periodic tasks. By effectively utilizing the periodic structure within data, SimPer achieves notable improvements over existing methods. The approach is both simple and versatile, offering a promising direction for future research in self-supervised learning for structured data and periodic signal analysis.