Building a Generative Adversarial Network for Image Synthesis

B. Y. Chandra
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Abstract

Generative Adversarial Networks (GANs) have emerged as a powerful class of generative models, capable of synthesizing realistic images by leveraging adversarial training. It explores the process of building a Generative Adversarial Network for image synthesis, delving into the underlying architecture, training methodology, and potential applications. Generative Adversarial Networks typically run unsupervised and use a cooperative zero- sum game framework to learn, where one person's gain equals another person's loss. The proposed Generative Adversarial Network architecture consists of a generator network that learns to create images from random noise and a discriminator network trained to distinguish between real and generated images. Through an adversarial training process, these networks iteratively refine their capabilities, resulting in a generator that produces increasingly realistic pictures and a discriminator with enhanced discriminative abilities. Generative Adversarial Networks are an effective tool for producing realistic, high-quality outputs in a variety of fields, including text and image generation, because of this back-and- forth competition, which results in the creation of increasingly convincing and indistinguishable synthetic data.
为图像合成构建生成式对抗网络
生成对抗网络(GAN)是一类功能强大的生成模型,能够通过对抗训练合成逼真的图像。该书探讨了为图像合成构建生成对抗网络的过程,深入研究了其基本架构、训练方法和潜在应用。生成式对抗网络通常在无监督的情况下运行,并使用合作零和博弈框架进行学习,即一个人的收益等于另一个人的损失。拟议的生成式对抗网络架构由一个生成器网络和一个判别器网络组成,生成器网络可学习从随机噪音中生成图像,而判别器网络则经过训练,可区分真实图像和生成的图像。通过对抗训练过程,这些网络不断完善自身能力,最终生成器生成的图像越来越逼真,而鉴别器的鉴别能力也越来越强。生成式对抗网络是在文本和图像生成等多个领域生成逼真、高质量输出结果的有效工具,因为这种来回竞争的结果是创建出越来越令人信服和难以区分的合成数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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