一个为所有:以单一风格实现通用域翻译GANAN
概括
UniTranslator有效地翻译不同领域的视觉表示,即使数据有限. 这种新型模型利用CLIP和一个新的映射器来实现高质量,多样化的图像翻译,优于现有方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 跨领域的视觉翻译是具有挑战性的,因为数据有限和显著的视觉差异.
- 现有的模型往往难以处理各种视觉领域,需要大量的训练数据.
研究的目的:
- 提出UniTranslator,这是一个新的翻译模型,用于在视觉上不同的领域之间转换表示.
- 为了解决有限的培训数据和跨领域翻译中的显著视觉差异的局限性.
主要方法:
- 利用CLIP的域中立能力作为一个桥梁机制.
- 使用单独的模块提取抽象的,域异的语义.
- 引入一个CLIP2P映射器来弥合CLIP和StyleGAN隐藏空间.
主要成果:
- UniTranslator 实现了具有域相关性和多样性的高质量翻译.
- 该模型在具有挑战性的跨领域场景中展示了更好的图像质量.
- UniTranslator超越了通用模型,并与专业模型竞争.
结论:
- UniTranslator为各种翻译任务提供了一种多功能解决方案,包括风格混合和风格化.
- 该模型的方法有效地处理了显著的视觉差异和有限的数据.
- 这项工作推进了跨领域的视觉表示转换.
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