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In order to train robust deep learning models, large amounts of labelled datais required. However, in the absence of such large repositories of labelleddata, unlabeled data can be exploited for the same. Semi-Supervised learningaims to utilize such unlabeled data for training classification models. Recentprogress of self-training based approaches have shown promise in this area,which leads to this study where we utilize an ensemble approach for the same. Aby-product of any semi-supervised approach may be loss of calibration of thetrained model especially in scenarios where unlabeled data may containout-of-distribution samples, which leads to this investigation on how to adaptto such effects. Our proposed algorithm carefully avoids common pitfalls inutilizing unlabeled data and leads to a more accurate and calibrated supervisedmodel compared to vanilla self-training based student-teacher algorithms. Weperform several experiments on the popular STL-10 database followed by anextensive analysis of our approach and study its effects on model accuracy andcalibration.

Self Training with Ensemble of Teacher Models