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One of the major obstacles that has to be faced when applying automatic emotion recognition to realistic human-machine interaction systems is the scarcity of labeled data for training a robust model.Motivated by this concern, this paper seeks to utmost exploit unlabeled data that are pervasively available in the real-world and easy to be collected,
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