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Synthetic Water Crystal Image Generation Using VAE-GANs and Diffusion Models

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Smart Mobile Communication & Artificial Intelligence (IMCL 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 936))

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Abstract

In recent years, remarkable progress has been made in generative models, particularly in the fields of computer vision and natural language processing. The ability of generative models to generate new and diverse samples has resulted in a wide range of applications, such as image and video synthesis, text generation, and music composition. This study investigates generative modeling advances made using Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models. Although VAEs and GANs have been widely used for generative modeling tasks in the past, diffusion models have recently emerged as state-of-the-art models. This study provides a detailed analysis of each model, including its strengths and limitations, as well as its applications in image synthesis and video generation. Furthermore, this paper discusses recent developments in diffusion models such as denoising. Finally, this paper implements these proposed models to generate water crystal images.

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Acknowledgement

I would like to extend my sincere appreciation to several individuals who have contributed significantly to the completion of this research paper. First and foremost, we are grateful to Dr. Andreas Pester and Dr. Frederic Andres, for their unwavering dedication, expertise, and collaborative spirit. Their contributions were invaluable in shaping the ideas and content presented in this paper. The successful completion of this paper is the result of the collective efforts of these individuals, and it is deeply appreciated for their contributions. We would like to thank the National Institute of Informatics (Tokyo, Japan) for the support of this research.

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Correspondence to Farah Aymen .

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Aymen, F., Pester, A., Andres, F. (2024). Synthetic Water Crystal Image Generation Using VAE-GANs and Diffusion Models. In: Auer, M.E., Tsiatsos, T. (eds) Smart Mobile Communication & Artificial Intelligence. IMCL 2023. Lecture Notes in Networks and Systems, vol 936. Springer, Cham. https://doi.org/10.1007/978-3-031-54327-2_10

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  • DOI: https://doi.org/10.1007/978-3-031-54327-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-54326-5

  • Online ISBN: 978-3-031-54327-2

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