Generative networks are fundamentally different in their aim and methods compared to CNNs for classification, segmentation, or object detection. They have initially not been meant to be an image analysis tool, but to produce naturally looking images. The adversarial training paradigm has been proposed to stabilize generative methods, and has proven to be highly successful -- though by no means from the first attempt. This chapter gives a basic introduction into the motivation for Generative Adversarial Networks (GANs) and traces the path of their success by abstracting the basic task and working mechanism, and deriving the difficulty of early practical approaches. Methods for a more stable training will be shown, and also typical signs for poor convergence and their reasons. Though this chapter focuses on GANs that are meant for image generation and image analysis, the adversarial training paradigm itself is not specific to images, and also generalizes to tasks in image analysis. Examples of architectures for image semantic segmentation and abnormality detection will be acclaimed, before contrasting GANs with further generative modeling approaches lately entering the scene. This will allow a contextualized view on the limits but also benefits of GANs.
翻译:与CNN的分类、分解或对象探测的目的和方法相比,产生网络在目的和方法上与CNN在分类、分解或对象探测方面有根本的不同,它们最初不是用来作为图像分析工具的,而是用来制作自然的图像。对抗性培训范式是为了稳定基因分析方法而提出的,虽然没有第一次尝试过,但证明非常成功。本章对基因反向网络(GANs)的动机进行了基本介绍,并用抽象的基本任务和工作机制以及早期实用方法的难度来追踪成功的道路。将展示更稳定的培训方法,以及趋同性差的典型迹象及其原因。虽然本章侧重于用于图像生成和图像分析的GANs,但对抗性培训范式本身并非针对图像,而是概括图像分析中的任务。在将GANs与最近进入现场的进一步的基因化模型模型方法进行比较之前,将先以图象性分解和异常性检测的架构为表态。这将允许对GANs的局限性进行背景化的视角,但也有利于GANs的效益。