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Books > Medicine > Nursing & ancillary services > Biomedical engineering
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Generative Adversarial Networks for Image-to-Image Translation (Paperback)
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Generative Adversarial Networks for Image-to-Image Translation (Paperback)
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Generative Adversarial Networks (GAN) have started a revolution in
Deep Learning, and today GAN is one of the most researched topics
in Artificial Intelligence. Generative Adversarial Networks for
Image-to-Image Translation provides a comprehensive overview of the
GAN (Generative Adversarial Network) concept starting from the
original GAN network to various GAN-based systems such as Deep
Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN,
Wasserstein GANs (WGAN), cyclical GANs, and many more. The book
also provides readers with detailed real-world applications and
common projects built using the GAN system with respective Python
code. A typical GAN system consists of two neural networks, i.e.,
generator and discriminator. Both of these networks contest with
each other, similar to game theory. The generator is responsible
for generating quality images that should resemble ground truth,
and the discriminator is accountable for identifying whether the
generated image is a real image or a fake image generated by the
generator. Being one of the unsupervised learning-based
architectures, GAN is a preferred method in cases where labeled
data is not available. GAN can generate high-quality images, images
of human faces developed from several sketches, convert images from
one domain to another, enhance images, combine an image with the
style of another image, change the appearance of a human face image
to show the effects in the progression of aging, generate images
from text, and many more applications. GAN is helpful in generating
output very close to the output generated by humans in a fraction
of second, and it can efficiently produce high-quality music,
speech, and images.
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