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Rank Supervised Contrastive Learning for Time Series Classification
Recently, various contrastive learning techniques have been developed to categorize time series data and exhibit promising performance.
Ordinal-Quadruplet: Retrieval of Missing Classes in Ordinal Time Series
In this paper, we propose an ordered time series classification framework that is robust against missing classes in the training data, i. e., during testing we can prescribe classes that are missing during training.
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