Recent studies on multi-domain facial image translation have achieved impressive results. The existing methods generally provide a discriminator with an auxiliary classifier to impose domain translation. However, these methods neglect important information regarding domain distribution matching. To solve this problem, we propose a switch generative adversarial network (SwitchGAN) with a more adaptive discriminator structure and a matched generator to perform delicate image translation among multiple domains. A feature-switching operation is proposed to achieve feature selection and fusion in our conditional modules. We demonstrate the effectiveness of our model. Furthermore, we also introduce a new capability of our generator that represents attribute intensity control and extracts content information without tailored training. Experiments on the Morph, RaFD and CelebA databases visually and quantitatively show that our extended SwitchGAN (i.e., Gated SwitchGAN) can achieve better translation results than StarGAN, AttGAN and STGAN. The attribute classification accuracy achieved using the trained ResNet-18 model and the FID score obtained using the ImageNet pretrained Inception-v3 model also quantitatively demonstrate the superior performance of our models.

本文提出了SwitchGAN来进行多领域人脸图像翻译。使用更自适应的辨别器结构和匹配的生成器来解决现有方法中存在的领域分布匹配的问题。引入基于特征切换的操作来进行特征选择和融合。实验结果表明，我们的模型在Morph、RaFD和CelebA数据库上均能取得比StarGAN、AttGAN和STGAN更好的翻译结果。此外，我们还引入了一种控制属性强度并提取内容信息的新功能。

多领域人脸图像翻译的门控 SwitchGAN