How to generate conditional synthetic data for a domain without utilizing
information about its labels/attributes? Our work presents a solution to the
above question. We propose a transfer learning-based framework utilizing
normalizing flows, coupled with both maximum-likelihood and adversarial
training. We model a source domain (labels available) and a target domain
(labels unavailable) with individual normalizing flows, and perform domain
alignment to a common latent space using adversarial discriminators. Due to the
invertible property of flow models, the mapping has exact cycle consistency. We
also learn the joint distribution of the data samples and attributes in the
source domain by employing an encoder to map attributes to the latent space via
adversarial training. During the synthesis phase, given any combination of
attributes, our method can generate synthetic samples conditioned on them in
the target domain. Empirical studies confirm the effectiveness of our method on
benchmarked datasets. We envision our method to be particularly useful for
synthetic data generation in label-scarce systems by generating non-trivial
augmentations via attribute transformations. These synthetic samples will
introduce more entropy into the label-scarce domain than their geometric and
photometric transformation counterparts, helpful for robust downstream tasks.

提出了一种基于转移学习的框架，利用正则化流和最大似然以及对抗性训练，模拟一个具有标签可用的源领域和没有标签可用的目标领域，采用对抗性辨别器将数据对齐到一个公共的潜空间中，通过编码器映射属性到潜在空间，并生成针对目标领域的特定属性的条件合成样本。

CDCGen：通过标准化流和对抗训练实现跨领域条件生成

CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and  Adversarial Training

Controllable semantic image editing enables a user to change entire image
attributes with a few clicks, e.g., gradually making a summer scene look like
it was taken in winter. Classic approaches for this task use a Generative
Adversarial Net (GAN) to learn a latent space and suitable latent-space
transformations. However, current approaches often suffer from attribute edits
that are entangled, global image identity changes, and diminished
photo-realism. To address these concerns, we learn multiple attribute
transformations simultaneously, integrate attribute regression into the
training of transformation functions, and apply a content loss and an
adversarial loss that encourages the maintenance of image identity and
photo-realism. We propose quantitative evaluation strategies for measuring
controllable editing performance, unlike prior work, which primarily focuses on
qualitative evaluation. Our model permits better control for both single- and
multiple-attribute editing while preserving image identity and realism during
transformation. We provide empirical results for both natural and synthetic
images, highlighting that our model achieves state-of-the-art performance for
targeted image manipulation.

本研究提出了一种新的控制图像属性的编辑方法，通过学习多种属性变换和应用内容损失和对抗损失来实现单一和多属性的编辑，并保留图像身份和真实感。此外，提出了客观评价标准以衡量模型性能，结果表明该模型在目标图像操作方面达到了最先进水平。