Style Transfer Generator for Dataset Testing Classification

Authors

  • Bayu Yasa Wedha Pradita University
  • Daniel Avian Karjadi Universitas Pradita
  • Alessandro Enriqco Putra Bayu Wedha Universitas Pradita
  • Handri Santoso Universitas Pradita

DOI:

10.33395/sinkron.v7i2.11375

Keywords:

Generative Adversarial Networks, Convolutional Neural Network, Style Transfer, Image Dataset, Art Image

Abstract

The development of the Generative Adversarial Network is currently very fast. First introduced by Ian Goodfellow in 2014, its development has accelerated since 2018. Currently, the need for datasets is sometimes still lacking, while public datasets are sometimes still lacking in number. This study tries to add an image dataset for supervised learning purposes. However, the dataset that will be studied is a unique dataset, not a dataset from the camera. But the image dataset by doing the augmented process by generating from the existing image. By adding a few changes to the augmentation process. So that the image datasets become diverse, not only datasets from camera photos but datasets that are carried out with an augmented process. Camera photos added with painting images will become still images with a newer style. There are many studies on Style transfer to produce images in drawing art, but it is possible to generate images for the needs of image datasets. The resulting force transfer image data set was used as the test data set for the Convolutional Neural Network classification. Classification can also be used to detect specific objects or images. The image dataset resulting from the style transfer is used for the classification of goods transporting vehicles or trucks. Detection trucks are very useful in the transportation system, where currently many trucks are modified to avoid road fees

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References

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How to Cite

Wedha, B. Y., Karjadi, D. A. ., Wedha, A. E. P. B. ., & Santoso, H. . (2022). Style Transfer Generator for Dataset Testing Classification. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 6(2), 448-454. https://doi.org/10.33395/sinkron.v7i2.11375

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