GANs in Action: Deep learning with Generative Adversarial Networks

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Publisher ‏ : ‎ Manning; First Edition (October 8, 2019)
Language ‏ : ‎ English
Paperback ‏ : ‎ 276 pages
ISBN-10 ‏ : ‎ 1617295566
ISBN-13 ‏ : ‎ 978-1617295560
Item Weight ‏ : ‎ 14.7 ounces
Dimensions ‏ : ‎ 7.38 x 0.4 x 9.25 inches

Generative Adversarial Networks (GANs) have revolutionized the field of deep learning by enabling the generation of realistic and high-quality synthetic data. In this post, we will explore how GANs work and showcase some examples of GANs in action.

GANs consist of two neural networks – a generator and a discriminator – that are trained simultaneously in a competitive manner. The generator aims to create realistic data samples, while the discriminator tries to distinguish between real and fake data. Through this adversarial process, the generator improves its ability to generate realistic data, leading to impressive results in image generation, text-to-image synthesis, and more.

One popular application of GANs is in image generation, where they can create photorealistic images of faces, landscapes, and even artwork. For example, StyleGAN2 has been used to generate highly realistic portraits of people that are indistinguishable from real photographs.

Another exciting use case of GANs is in data augmentation, where synthetic data is generated to supplement limited training datasets. This can help improve the performance of deep learning models, especially in scenarios with scarce or imbalanced data.

Overall, GANs have opened up new possibilities in deep learning, allowing for the creation of realistic data samples and enhancing the capabilities of machine learning models. As the field continues to advance, we can expect to see even more impressive applications of GANs in various domains.
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