Unpaired Image Translation via Vector Symbolic Architectures

Justin D. Theiss, Jay Leverett, Daeil Kim, Aayush Prakash
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引用次数: 14

Abstract

Image-to-image translation has played an important role in enabling synthetic data for computer vision. However, if the source and target domains have a large semantic mismatch, existing techniques often suffer from source content corruption aka semantic flipping. To address this problem, we propose a new paradigm for image-to-image translation using Vector Symbolic Architectures (VSA), a theoretical framework which defines algebraic operations in a high-dimensional vector (hypervector) space. We introduce VSA-based constraints on adversarial learning for source-to-target translations by learning a hypervector mapping that inverts the translation to ensure consistency with source content. We show both qualitatively and quantitatively that our method improves over other state-of-the-art techniques.
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基于矢量符号结构的非配对图像翻译
图像到图像的转换在为计算机视觉提供合成数据方面发挥了重要作用。然而,如果源域和目标域有很大的语义不匹配,现有的技术往往会遭受源内容损坏,即语义翻转。为了解决这个问题,我们提出了一个使用向量符号体系结构(VSA)的图像到图像转换的新范式,VSA是一个理论框架,它定义了高维向量(超向量)空间中的代数运算。我们引入了基于vsa的对抗性学习约束,通过学习一个反向翻译的超向量映射来确保与源内容的一致性。我们在定性和定量上都表明,我们的方法优于其他最先进的技术。
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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