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• W3092747519 abstract "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." @default.
• W3092747519 created "2020-10-22" @default.
• W3092747519 creator A5024037777 @default.
• W3092747519 date "2020-10-16" @default.
• W3092747519 modified "2023-10-05" @default.
• W3092747519 title "Deep Generative Adversarial Networks for Image-to-Image Translation: A Review" @default.
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• W3092747519 doi "https://doi.org/10.3390/sym12101705" @default.
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