Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for this task: (1) lack of aligned training pairs and (2) multiple possible outputs from a single input image. In this work, we present an approach based on disentangled representation for generating diverse outputs without paired training images. To synthesize diverse outputs, we propose to embed images onto two spaces: a domain-invariant content space capturing shared information across domains and a domain-specific attribute space. Our model takes the encoded content features extracted from a given input and attribute vectors sampled from the attribute space to synthesize diverse outputs at test time. To handle unpaired training data, we introduce a cross-cycle consistency loss based on disentangled representations. Qualitative results show that our model can generate diverse and realistic images on a wide range of tasks without paired training data. For quantitative evaluations, we measure realism with user study and Fréchet inception distance, and measure diversity with the perceptual distance metric, Jensen–Shannon divergence, and number of statistically-different bins.

中文翻译：

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DRIT++：通过解开表示实现多样化的图像到图像转换

图像到图像的翻译旨在学习两个视觉域之间的映射。这项任务有两个主要挑战：（1）缺乏对齐的训练对和（2）来自单个输入图像的多个可能的输出。在这项工作中，我们提出了一种基于解开表示的方法，用于在没有配对训练图像的情况下生成不同的输出。为了合成不同的输出，我们建议将图像嵌入到两个空间中：一个域不变的内容空间，捕获跨域的共享信息，一个域特定的属性空间。我们的模型采用从给定输入中提取的编码内容特征和从属性空间采样的属性向量，以在测试时合成不同的输出。为了处理不成对的训练数据，我们引入了基于解开表示的跨周期一致性损失。定性结果表明，我们的模型可以在没有配对训练数据的情况下在各种任务上生成多样化和逼真的图像。对于定量评估，我们使用用户研究和 Fréchet 初始距离来衡量真实性，并使用感知距离度量、Jensen-Shannon 散度和统计上不同的 bin 数量来衡量多样性。