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Deep Generative Adversarial Networks for Image-to-Image Translation: A Review
Symmetry ( IF 2.940 ) Pub Date : 2020-10-16 , DOI: 10.3390/sym12101705
Aziz Alotaibi

Many image processing, computer graphics, and computer vision problems can be treated as image-to-image translation tasks. Such translation entails learning to map one visual representation of a given input to another representation. Image-to-image translation with generative adversarial networks (GANs) has been intensively studied and applied to various tasks, such as multimodal image-to-image translation, super-resolution translation, object transfiguration-related translation, etc. However, image-to-image translation techniques suffer from some problems, such as mode collapse, instability, and a lack of diversity. This article provides a comprehensive overview of image-to-image translation based on GAN algorithms and its variants. It also discusses and analyzes current state-of-the-art image-to-image translation techniques that are based on multimodal and multidomain representations. Finally, open issues and future research directions utilizing reinforcement learning and three-dimensional (3D) modal translation are summarized and discussed.

中文翻译:

用于图像到图像转换的深度生成对抗网络:综述

许多图像处理、计算机图形学和计算机视觉问题都可以被视为图像到图像的转换任务。这种翻译需要学习将给定输入的一种视觉表示映射到另一种表示。使用生成对抗网络 (GAN) 的图像到图像翻译已被深入研究并应用于各种任务,例如多模态图像到图像翻译、超分辨率翻译、与对象变形相关的翻译等。图像转换技术存在一些问题,例如模式崩溃、不稳定和缺乏多样性。本文全面概述了基于 GAN 算法及其变体的图像到图像转换。它还讨论和分析了当前最先进的基于多模态和多域表示的图像到图像转换技术。最后,总结和讨论了利用强化学习和三维(3D)模态翻译的开放问题和未来的研究方向。
更新日期:2020-10-16
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