论文标题
矢量量化图像到图像翻译
Vector Quantized Image-to-Image Translation
论文作者
论文摘要
当前的图像到图像翻译方法通过条件生成模型来制定任务，从而仅学习重塑或区域变化，这受到条件上下文提供的丰富结构信息的约束。在这项工作中，我们建议将矢量量化技术引入图像到图像翻译框架。矢量量化的内容表示不仅可以促进翻译，还可以促进不同域之间共享的无条件分布。同时，加上散布的样式表示，该方法进一步使图像扩展的能力具有灵活性，并在内部和间域间具有灵活性。定性和定量实验表明，我们的框架与最先进的图像到图像到图像转换和图像扩展方法的性能可比。与单个任务的方法相比，所提出的方法作为统一框架，释放了结合图像到图像翻译，无条件生成和图像扩展的应用程序。例如，它为图像生成和扩展提供了样式的可变性，并为图像到图像翻译与进一步的扩展功能进行了更高的功能。
Current image-to-image translation methods formulate the task with conditional generation models, leading to learning only the recolorization or regional changes as being constrained by the rich structural information provided by the conditional contexts. In this work, we propose introducing the vector quantization technique into the image-to-image translation framework. The vector quantized content representation can facilitate not only the translation, but also the unconditional distribution shared among different domains. Meanwhile, along with the disentangled style representation, the proposed method further enables the capability of image extension with flexibility in both intra- and inter-domains. Qualitative and quantitative experiments demonstrate that our framework achieves comparable performance to the state-of-the-art image-to-image translation and image extension methods. Compared to methods for individual tasks, the proposed method, as a unified framework, unleashes applications combining image-to-image translation, unconditional generation, and image extension altogether. For example, it provides style variability for image generation and extension, and equips image-to-image translation with further extension capabilities.
