Pseudo-labeling based adaptations of pain domain classifiers

The results of the study show that it is beneficial to augment the training set with the pseudo labeled long-term domain samples. For the electric pain domain in combination with the early fusion approach, we improved the classification performance by 2.4% to 80.4% in comparison to the basic approach. For the thermal pain domain in combination with the early fusion approach, we improved the classification performance by 2.8% to 70.0% in comparison to the basic approach.