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arXiv · 2002.08641

A Novel Framework for Selection of GANs for an Application

Tanya Motwani·Manojkumar Parmar
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Abstract

Generative Adversarial Network (GAN) is a current focal point of research. The body of knowledge is fragmented, leading to a trial-error method while selecting an appropriate GAN for a given scenario. We provide a comprehensive summary of the evolution of GANs starting from its inception addressing issues like mode collapse, vanishing gradient, unstable training and non-convergence. We also provide a comparison of various GANs from the application point of view, its behaviour and implementation details. We propose a novel framework to identify candidate GANs for a specific use case based on architecture, loss, regularization and divergence. We also discuss application of the framework using an example, and we demonstrate a significant reduction in search space. This efficient way to determine potential GANs lowers unit economics of AI development for organizations.

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Tanya Motwani, Manojkumar Parmar. 2021-05-17. A Novel Framework for Selection of GANs for an Application. https://arxiv.org/abs/2002.08641

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