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By identifying similarities between successive inputs, Self-SupervisedLearning (SSL) methods for time series analysis have demonstrated theireffectiveness in encoding the inherent static characteristics of temporal data.However, an exclusive emphasis on similarities might result in representationsthat overlook the dynamic attributes critical for modeling cardiovasculardiseases within a confined subject cohort. Introducing Distilled EncodingBeyond Similarities (DEBS), this paper pioneers an SSL approach that transcendsmere similarities by integrating dissimilarities among positive pairs. Theframework is applied to electrocardiogram (ECG) signals, leading to a notableenhancement of +10\% in the detection accuracy of Atrial Fibrillation (AFib)across diverse subjects. DEBS underscores the potential of attaining a morerefined representation by encoding the dynamic characteristics of time seriesdata, tapping into dissimilarities during the optimization process. Broadly,the strategy delineated in this study holds the promise of unearthing novelavenues for advancing SSL methodologies tailored to temporal data.

Learning Beyond Similarities: Incorporating Dissimilarities between Positive Pairs in Self-Supervised Time Series Learning