  Generative Adversarial Networks (GANs) are well-known tools for data
generation and semi-supervised classification. GANs, with less labeled data,
outperform Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs)
in classification across various tasks, this shows promise for developing GANs
capable of trespassing into the domain of semi-supervised regression. However,
developing GANs for regression introduce two major challenges: (1) inherent
instability in the GAN formulation and (2) performing regression and achieving
stability simultaneously. This paper introduces techniques that show
improvement in the GANs' regression capability through mean absolute error
(MAE) and mean squared error (MSE). We bake a differentiable fuzzy logic system
at multiple locations in a GAN because fuzzy logic systems have demonstrated
high efficacy in classification and regression settings. The fuzzy logic takes
the output of either or both the generator and the discriminator to either or
both predict the output, $y$, and evaluate the generator's performance. We
outline the results of applying the fuzzy logic system to CGAN and summarize
each approach's efficacy. This paper shows that adding a fuzzy logic layer can
enhance GAN's ability to perform regression; the most desirable injection
location is problem-specific, and we show this through experiments over various
datasets. Besides, we demonstrate empirically that the fuzzy-infused GAN is
competitive with DNNs.
