In this work, we generalize semi-supervised generative adversarial networks
(GANs) from classification problems to regression problems. In the last few
years, the importance of improving the training of neural networks using
semi-supervised training has been demonstrated for classification problems. We
present a novel loss function, called feature contrasting, resulting in a
discriminator which can distinguish between fake and real data based on feature
statistics. This method avoids potential biases and limitations of alternative
approaches. The generalization of semi-supervised GANs to the regime of
regression problems of opens their use to countless applications as well as
providing an avenue for a deeper understanding of how GANs function. We first
demonstrate the capabilities of semi-supervised regression GANs on a toy
dataset which allows for a detailed understanding of how they operate in
various circumstances. This toy dataset is used to provide a theoretical basis
of the semi-supervised regression GAN. We then apply the semi-supervised
regression GANs to a number of real-world computer vision applications: age
estimation, driving steering angle prediction, and crowd counting from single
images. We perform extensive tests of what accuracy can be achieved with
significantly reduced annotated data. Through the combination of the
theoretical example and real-world scenarios, we demonstrate how
semi-supervised GANs can be generalized to regression problems.

该论文将半监督生成对抗网络从分类问题推广到回归问题，并提出了特征对比的新损失函数，旨在提高神经网络训练的准确率，可以用于计算机视觉等领域的实际应用。