Fuzzy Generative Adversarial Networks

• URL: http://arxiv.org/abs/2110.14588v1
• Date: Wed, 27 Oct 2021 17:05:06 GMT
• Title: Fuzzy Generative Adversarial Networks
• Authors: Ryan Nguyen, Shubhendu Kumar Singh, and Rahul Rai
• Abstract summary: Generative Adversarial Networks (GANs) are well-known tools for data generation and semi-supervised classification. This paper introduces techniques that show improvement in the GANs' regression capability through mean absolute error (MAE) and mean squared error (MSE) We show that adding a fuzzy logic layer can enhance GAN's ability to perform regression; the most desirable injection location is problem-specific.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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