Generative Adversarial Neural Networks Applied to Image Generation

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Publisher ‏ : ‎ LAP LAMBERT Academic Publishing (July 9, 2019)
Language ‏ : ‎ English
Paperback ‏ : ‎ 124 pages
ISBN-10 ‏ : ‎ 6200239975
ISBN-13 ‏ : ‎ 978-6200239976
Item Weight ‏ : ‎ 7.2 ounces
Dimensions ‏ : ‎ 5.91 x 0.28 x 8.66 inches

Generative Adversarial Neural Networks Applied to Image Generation

Generative Adversarial Networks (GANs) have revolutionized the field of image generation by pitting two neural networks against each other in a game-theoretic framework. One network, the generator, creates realistic images, while the other network, the discriminator, tries to distinguish between real and generated images. Through this adversarial process, the generator learns to create increasingly convincing images, while the discriminator improves its ability to differentiate between real and fake images.

GANs have been successfully applied in a wide range of image generation tasks, including generating photorealistic images, creating artistic designs, and enhancing low-resolution images. The ability of GANs to learn complex, high-dimensional distributions makes them particularly well-suited for generating diverse and realistic images.

One of the key strengths of GANs is their ability to generate images that contain realistic details and textures, making them indistinguishable from real images to the human eye. This has enabled applications such as generating high-quality images for virtual reality, video games, and computer-generated imagery in movies.

Despite their impressive capabilities, GANs also face challenges, such as mode collapse, where the generator produces limited variations of similar images, and instability during training. Researchers are actively working on improving the performance and stability of GANs through techniques such as regularizing the training process, designing more effective network architectures, and optimizing the training algorithms.

As GANs continue to evolve, they hold great promise for revolutionizing image generation across a wide range of domains, from computer vision and graphics to art and design. The ability of GANs to create highly realistic and diverse images opens up new possibilities for creative expression and innovation in image generation.
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