Self-supervised learning (SSL) has had great success in both computer vision
and natural language processing. These approaches often rely on cleverly
crafted loss functions and training setups to avoid feature collapse. In this
study, the effectiveness of mainstream SSL frameworks from computer vision and
some SSL frameworks for time series are evaluated on the UCR, UEA and PTB-XL
datasets, and we show that computer vision SSL frameworks can be effective 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 IterNorm layer that accelerates the convergence of the model