Revolution in Image Data Collection: CycleGAN as a Dataset Generator

Authors

  • Djarot Hindarto Information System Study Program, Faculty of Communication and Informatics Technology, Universitas Nasional, Jakarta, Indonesia https://orcid.org/0000-0001-7501-2610
  • Endah Tri Esti Handayani Information System Study Program, Faculty of Communication and Informatics Technology, Universitas Nasional, Jakarta, Indonesia

DOI:

10.33395/sinkron.v9i1.13211

Keywords:

Computer Vision, CycleGAN, Deep Learning, Generative Adversarial Network, Transform Images

Abstract

Computer vision, deep learning, and pattern recognition are just a few fields where image data collection has become crucial. The Cycle Generative Adversarial Network has become one of the most effective instruments in the recent revolution in image data collection. This research aims to comprehend the impact of CycleGAN on the collection of image datasets. CycleGAN, a variant of the Generative Adversarial Network model, has enabled the unprecedented generation of image datasets. CycleGAN can transform images from one domain to another without manual annotation by employing adversarial learning between the generator and discriminator. This means generating image datasets quickly and efficiently for various purposes, from object recognition to data augmentation. One of the most fascinating features of CycleGAN is its capacity to alter an image's style and characteristics. Using CycleGAN to generate unique and diverse datasets assists deep learning models in overcoming visual style differences. This is a significant development in understanding how machine learning models can comprehend visual art concepts. CycleGAN's use as a data set generator has altered the landscape of image data collection. CycleGAN has opened new doors in technological innovation and data science with its proficiency in generating diverse and unique datasets. This research will investigate in greater detail how CycleGAN revolutionized the collection of image datasets and inspired previously unconceived applications.

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References

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How to Cite

Hindarto, D., & Handayani, E. T. E. . (2024). Revolution in Image Data Collection: CycleGAN as a Dataset Generator. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(1), 444-454. https://doi.org/10.33395/sinkron.v9i1.13211

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