Deep generative models have been applied to multiple applications in image-
to-image translation. Generative Adversarial Networks and Diffusion Models have
presented impressive results, setting new state-of-the-art results on these
tasks. Most methods have symmetric setups across the different domains in a
dataset. These methods assume that all domains have either multiple modalities
or only one modality. However, there are many datasets that have a many-to-one
relationship between two domains. In this work, we first introduce a Colorized
MNIST dataset and a Color-Recall score that can provide a simple benchmark for
evaluating models on many-to-one translation. We then introduce a new
asymmetric framework to improve existing deep generative models on many-to-one
image-to- image translation. We apply this framework to StarGAN V2 and show
that in both unsupervised and semi-supervised settings, the performance of this
new model improves on many-to-one image-to-image translation.

通过引入 Colorized MNIST 数据集和 Color-Recall 评分，提出一种新的非对称框架以改进现有的深度生成模型在多对一图像翻译任务中的性能，应用于 StarGAN V2，并在无监督和半监督设置下表现出更好的结果。

改进多对一图像到图像转换的深度生成模型

Improving Deep Generative Models on Many-To-One Image-to-Image  Translation

A good image-to-image translation model should learn a mapping between
different visual domains while satisfying the following properties: 1)
diversity of generated images and 2) scalability over multiple domains.
Existing methods address either of the issues, having limited diversity or
multiple models for all domains. We propose StarGAN v2, a single framework that
tackles both and shows significantly improved results over the baselines.
Experiments on CelebA-HQ and a new animal faces dataset (AFHQ) validate our
superiority in terms of visual quality, diversity, and scalability. To better
assess image-to-image translation models, we release AFHQ, high-quality animal
faces with large inter- and intra-domain differences. The code, pretrained
models, and dataset can be found at this https URL

提出了单个框架 StarGAN v2，用于解决图像到图像翻译模型中的多种领域转换问题，并在 CelebA-HQ 和新的动物面孔数据集（AFHQ）上进行了实验证明了其在视觉质量，多样性和可伸缩性方面的优越性。