Comparison and Analysis of Image-to-Image Generative Adversarial
Networks: A Survey
- URL: http://arxiv.org/abs/2112.12625v1
- Date: Thu, 23 Dec 2021 15:11:18 GMT
- Title: Comparison and Analysis of Image-to-Image Generative Adversarial
Networks: A Survey
- Authors: Sagar Saxena, Mohammad Nayeem Teli
- Abstract summary: Generative Adversarial Networks (GANs) have recently introduced effective methods of performing Image-to-Image translations.
In this paper, we survey and analyze eight Image-to-Image Generative Adversarial Networks: Pix2Px, CycleGAN, CoGAN, StarGAN, MUNIT, StarGAN2, DA-GAN, and Self Attention GAN.
Each of these models presented state-of-the-art results and introduced new techniques to build Image-to-Image GANs.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Generative Adversarial Networks (GANs) have recently introduced effective
methods of performing Image-to-Image translations. These models can be applied
and generalized to a variety of domains in Image-to-Image translation without
changing any parameters. In this paper, we survey and analyze eight
Image-to-Image Generative Adversarial Networks: Pix2Px, CycleGAN, CoGAN,
StarGAN, MUNIT, StarGAN2, DA-GAN, and Self Attention GAN. Each of these models
presented state-of-the-art results and introduced new techniques to build
Image-to-Image GANs. In addition to a survey of the models, we also survey the
18 datasets they were trained on and the 9 metrics they were evaluated on.
Finally, we present results of a controlled experiment for 6 of these models on
a common set of metrics and datasets. The results were mixed and showed that on
certain datasets, tasks, and metrics some models outperformed others. The last
section of this paper discusses those results and establishes areas of future
research. As researchers continue to innovate new Image-to-Image GANs, it is
important that they gain a good understanding of the existing methods,
datasets, and metrics. This paper provides a comprehensive overview and
discussion to help build this foundation.
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