Learning from periodic data is essential for many real-world applications, such as monitoring weather systems and detecting vital signs. However, traditional supervised approaches for learning periodic information require a large amount of labeled data, which can be challenging and resource-intensive to obtain. Self-supervised learning methods have shown promise in capturing periodic or quasi-periodic temporal dynamics using unlabeled data.
To address these challenges, we introduced SimPer, a self-supervised contrastive framework for learning periodic targets. SimPer leverages temporal self-contrastive learning, where positive and negative samples are obtained through periodicity-invariant and periodicity-variant augmentations. We also propose a periodic feature similarity that explicitly defines how to measure similarity in the context of periodic learning.
SimPer outperforms state-of-the-art self-supervised learning methods in terms of data efficiency, robustness to spurious correlations, and generalization. It can be applied to various domains, including human behavior analysis, environmental remote sensing, and healthcare. For example, it can accurately predict heart rates and count exercise repetitions from video data.
Overall, SimPer provides an intuitive and flexible approach for learning strong feature representations for periodic signals. It has the potential to improve many real-world applications and contribute to advancements in the field of artificial intelligence.
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