  Semi-supervised learning plays an important role in large-scale machine
learning. Properly using additional unlabeled data (largely available nowadays)
often can improve the machine learning accuracy. However, if the machine
learning model is misspecified for the underlying true data distribution, the
model performance could be seriously jeopardized. This issue is known as model
misspecification. To address this issue, we focus on generative models and
propose a criterion to detect the onset of model misspecification by measuring
the performance difference between models obtained using supervised and
semi-supervised learning. Then, we propose to automatically modify the
generative models during model training to achieve an unbiased generative
model. Rigorous experiments were carried out to evaluate the proposed method
using two image classification data sets PASCAL VOC'07 and MIR Flickr. Our
proposed method has been demonstrated to outperform a number of
state-of-the-art semi-supervised learning approaches for the classification
task.
