We present FusedGAN, a deep network for conditional image synthesis with
controllable sampling of diverse images. Fidelity, diversity and controllable
sampling are the main quality measures of a good image generation model. Most
existing models are insufficient in all three aspects. The FusedGAN can perform
controllable sampling of diverse images with very high fidelity. We argue that
controllability can be achieved by disentangling the generation process into
various stages. In contrast to stacked GANs, where multiple stages of GANs are
trained separately with full supervision of labeled intermediate images, the
FusedGAN has a single stage pipeline with a built-in stacking of GANs. Unlike
existing methods, which requires full supervision with paired conditions and
images, the FusedGAN can effectively leverage more abundant images without
corresponding conditions in training, to produce more diverse samples with high
fidelity. We achieve this by fusing two generators: one for unconditional image
generation, and the other for conditional image generation, where the two
partly share a common latent space thereby disentangling the generation. We
demonstrate the efficacy of the FusedGAN in fine grained image generation tasks
such as text-to-image, and attribute-to-face generation.

本文介绍了 FusedGAN，一种深度网络，用于具有可控多样性采样的条件图像合成。通过将生成过程解耦为各个阶段，它可以在很高的保真度下执行可控的多样性图像采样，并通过融合两个生成器来实现。我们在文本到图像和属性到人脸生成等细粒度图像生成任务中验证了 FusedGAN 的有效性。