Reg-Gan: Semi-Supervised Learning Based on Generative Adversarial Networks for Regression

This research concerns introducing a method to solve the semi-supervised learning problem with generative adversarial networks (GANs) for regression. In contrast to classification, where only a limited number of distinct classes is given, the regression task is defined as predicting continuous labels for a given dataset. This method will be of particular interest for the applications in which a small number of labeled samples is available, and the labels are continuous such as predicting steering angles from the front camera image in the end-to-end task of autonomous driving. Semi-supervised learning is of vital importance for the applications where a small number of labeled samples is available, or labeling samples is difficult or expensive to collect. A case in point is autonomous driving in which obtaining sufficient labeled samples covering all driving conditions is costly. In this context, we can take advantage of semi-supervised learning techniques with groundbreaking generative models, such as generative adversarial networks. However, currently almost all proposed GAN-based semi-supervised techniques in the literature are focused on solving the classification problem. Hence, developing a GAN-based semi-supervised method for the regression task is still an open problem. In this work, two different architectures will be proposed to address this problem. In summary, our introduced method is able to predict continuous labels for a training dataset which has only a limited number of labeled samples. Moreover, the application of this technique for solving the end-to-end task in autonomous driving will be presented. We performed several experiments to evaluate our proposed method, and the results are very promising compared with the state-of-the-art Improved-GAN technique [1].