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.

翻译：自监督学习（SSL）在计算机视觉领域取得了巨大成功。目前主流的计算机视觉SSL框架大多基于孪生网络架构，这些方法通常依赖巧妙设计的损失函数和训练机制来避免特征坍塌。本研究评估了这些计算机视觉SSL框架在不同模态（即时间序列）上的有效性。通过在UCR和UEA数据集上的实验验证，我们证明计算机视觉SSL框架对时间序列同样有效。此外，我们提出了一种改进方法，对近期提出的VICReg方法进行了优化：一方面改进了VICReg中的协方差项，另一方面在架构头部增加了迭代归一化层，从而加速模型收敛。