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article · International Journal for Research in Applied Science and Engineering Technology · 2022

GAN Based State-of-Art Image Colorization

Gundla Kavya

Cited by 0Open access

Abstract

Abstract: This project helps you to colorize the black and white pictures and it’s done by using GANs(Generative Adversarial Networks) .GANs generally consists of a generator and a discriminator or critic which are competitive with each other. Force of GAN brings tones-generator applies tones to the perceived objects trained on, and discriminator attempts to scrutinize the color choice. They can reduce and replace the loss function with the help of the network and solves the problem of realistic colorization .It is further improvised and utilizes another sort of GAN training technique called NoGAN which is created to solve fundamental issues that seemed while trained using normal Generative Adversarial Networks composed of discriminator and a generator. The dataset used is ImageNet and the model depends on fast.ai library and also the series of evolution of improving GANs utilisation is explained.

Cite this paper

APA 7

Kavya, G. (2022). GAN Based State-of-Art Image Colorization. International Journal for Research in Applied Science and Engineering Technology, 10(6), 3030–3034. https://doi.org/10.22214/ijraset.2022.44557

MLA 9

Kavya, Gundla. “GAN Based State-of-Art Image Colorization.” International Journal for Research in Applied Science and Engineering Technology, vol. 10, no. 6, 2022, pp. 3030–34, https://doi.org/10.22214/ijraset.2022.44557.

Chicago

Kavya, Gundla. 2022. “GAN Based State-of-Art Image Colorization.” International Journal for Research in Applied Science and Engineering Technology 10 (6): 3030–34. https://doi.org/10.22214/ijraset.2022.44557.

Harvard

Kavya, G. (2022) ‘GAN Based State-of-Art Image Colorization’, International Journal for Research in Applied Science and Engineering Technology, 10(6), pp. 3030–3034. Available at: https://doi.org/10.22214/ijraset.2022.44557.

BibTeX
@article{kavya2022based,
  title = {{GAN Based State-of-Art Image Colorization}},
  author = {Kavya, Gundla},
  journal = {International Journal for Research in Applied Science and Engineering Technology},
  volume = {10},
  number = {6},
  pages = {3030--3034},
  year = {2022},
  doi = {10.22214/ijraset.2022.44557}
}

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