FuzzyGAN: Fuzzy generative adversarial networks for regression tasks

Abstract

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 tasks. The success of GANs in classification tasks motivates the development of GAN-based techniques for semi-supervised regression tasks. However, developing GANs for regression introduces 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. We bake a differentiable fuzzy logic system at multiple locations in a GAN. The fuzzy logic takes the output of either the generator or the discriminator to predict the output, y, and evaluate the generator’s performance. We outline the results of applying the fuzzy logic system across multiple GANs 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 GANs are competitive with the DNNs.