Computer Science > Machine Learning
[Submitted on 2 Sep 2021 (v1), last revised 26 Jan 2024 (this version, v4)]
Title:Computer Vision Self-supervised Learning Methods on Time Series
View PDF HTML (experimental)Abstract:Self-supervised learning (SSL) has had great success in both computer vision. Most of the current mainstream computer vision SSL frameworks are based on Siamese network architecture. These approaches often rely on cleverly crafted loss functions and training setups to avoid feature collapse. In this study, we evaluate if those computer-vision SSL frameworks are also effective on a different modality (\textit{i.e.,} time series). The effectiveness is experimented and evaluated on the UCR and UEA archives, and we show that the computer vision SSL frameworks can be effective even for time series. In addition, we propose a new method that improves on the recently proposed VICReg method. Our method improves on a \textit{covariance} term proposed in VICReg, and in addition we augment the head of the architecture by an iterative normalization layer that accelerates the convergence of the model.
Submission history
From: Daesoo Lee [view email][v1] Thu, 2 Sep 2021 08:45:53 UTC (9,075 KB)
[v2] Fri, 3 Dec 2021 12:57:40 UTC (9,106 KB)
[v3] Mon, 18 Jul 2022 13:20:13 UTC (8,788 KB)
[v4] Fri, 26 Jan 2024 22:16:07 UTC (10,193 KB)
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