Mixing up contrastive learning: Self-supervised representation learning for time series

Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen

Abstract: Highlights•A contrastive learning framework for time series motivated through label smoothing.•An extensive evaluation of the proposed methodology with several baselines.•Proposed framework enables transfer learning for clinical time series.