Adversarial Sample Based Semi-Supervised Learning for Industrial Soft Sensor

Abstract

In industrial processes, soft sensor techniques are often utilized to predict the hard-to-measure quality variables. However, the labeled data which are obtained from the offline lab analysis can be quite rare. In the present work, a new divergence-based semi-supervised learning method is developed to exploit the unlabeled samples together with labeled ones for soft sensor application, namely adversarial tri-regression. First, the adversarial samples are generated based on the consideration of maximum disturbance, and through training on the combination of the adversarial samples and the original labeled samples, three regressors are initialized with divergence. Second, for each regressor, an unlabeled sample is labeled when the other two regressors agree on the labeling of this sample, which actually provides that regressor with some unknown information based on the divergence. As the three regressors label more and more samples for each other, the final regression model obtained by averaging the three base regressors presents increasingly more accurate prediction. The proposed method tackles a practical soft sensor problem for the industrial production process of cigarette.