What kind of Gan is needed for image translation?

What kind of Gan is needed for image translation?

The careful configuration of architecture as a type of image-conditional GAN allows for both the generation of large images compared to prior GAN models (e.g. such as 256×256 pixels) and the capability of performing well on a variety of different image-to-image translation tasks.

How to develop a pix2pix Gan for image-to-image translation?

This additional loss encourages the generator model to create plausible translations of the source image. The Pix2Pix GAN has been demonstrated on a range of image-to-image translation tasks such as converting maps to satellite photographs, black and white photographs to color, and sketches of products to product photographs.

How is discriminator trained for image to image translation?

The discriminator is provided both with a source image and the target image and must determine whether the target is a plausible transformation of the source image. The generator is trained via adversarial loss, which encourages the generator to generate plausible images in the target domain.

How can Gans be used to create artwork?

In this paper we further explore the use of GANs to create artwork by applying existing image to image trans- lation techniques to generate photos from sketches. The traditional implementations of GANs utilize dis- criminators that solely output a measurement of the real/fake quality, or realness, of an input image.

How to develop a pix2pix Gan for image-to-image?

The Pix2Pix model is a type of conditional GAN, or cGAN, where the generation of the output image is conditional on an input, in this case, a source image. The discriminator is provided both with a source image and the target image and must determine whether the target is a plausible transformation of the source image.

How does sketch to color image generation work?

Sketch to Color Image generation is an image-to-image translation model using Conditional Generative Adversarial Networks as described in the original paper by Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros 2016, Image-to-Image Translation with Conditional Adversarial Networks.