  One of the latest self-supervised learning (SSL) methods, VICReg, showed a
great performance both in the linear evaluation and the fine-tuning evaluation.
However, VICReg is proposed in computer vision and it learns by pulling
representations of random crops of an image while maintaining the
representation space by the variance and covariance loss. However, VICReg would
be ineffective on non-stationary time series where different parts/crops of
input should be differently encoded to consider the non-stationarity. Another
recent SSL proposal, Temporal Neighborhood Coding (TNC) is effective for
encoding non-stationary time series. This study shows that a combination of a
VICReg-style method and TNC is very effective for SSL on non-stationary time
series, where a non-stationary seismic signal time series is used as an
evaluation dataset.
