Learning Beyond Similarities: Incorporating Dissimilarities between
  Positive Pairs in Self-Supervised Time Series Learning
Adrian Atienza Jakob Bardram Sadasivan Puthusserypady
By identifying similarities between successive inputs, Self-Supervised
Learning (SSL) methods for time series analysis have demonstrated their
effectiveness in encoding the inherent static characteristics of temporal data.
However, an exclusive emphasis on similarities might result in representations
that overlook the dynamic attributes critical for modeling cardiovascular
diseases within a confined subject cohort. Introducing Distilled Encoding
Beyond Similarities (DEBS), this paper pioneers an SSL approach that transcends
mere similarities by integrating dissimilarities among positive pairs. The
framework is applied to electrocardiogram (ECG) signals, leading to a notable
enhancement of +10\% in the detection accuracy of Atrial Fibrillation (AFib)
across diverse subjects. DEBS underscores the potential of attaining a more
refined representation by encoding the dynamic characteristics of time series
data, tapping into dissimilarities during the optimization process. Broadly,
the strategy delineated in this study holds the promise of unearthing novel
avenues for advancing SSL methodologies tailored to temporal data.