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Self-Supervised Learning (SSL) is a new paradigm for learning discriminative representations without labelled data and has reached comparable or even state-of-the-art results in comparison to supervised counterparts.
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ESPRESSO: Entropy and ShaPe AwaRe TimE-Series SegmentatiOn for Processing Heterogeneous Sensor Data
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