The performance of image recognition like human pose detection, trained with simulated images would usually get worse due to the divergence between real and simulated data. To make the distribution of a simulated image close to that of real one, there are several works applying GAN-based image-to-image transformation methods, e.g., SimGAN and CycleGAN. However, these methods would not be sensitive enough to the various change in pose and shape of subjects, especially when the training data are imbalanced, e.g., some particular poses and shapes are minor in the training data. To overcome this problem, we propose to introduce the label information of subjects, e.g., pose and type of objects in the training of CycleGAN, and lead it to obtain label-wise transforamtion models. We evaluate our proposed method called Label-CycleGAN, through experiments on the digit image transformation from SVHN to MNIST and the surveillance camera image transformation from simulated to real images.

翻译：图像识别的性能,如人体姿势检测,经过模拟图像培训,通常会因为真实和模拟数据之间的差异而恶化。为了使模拟图像的分布接近真实图像的分布,有几部应用基于GAN图像到图像转换方法的作品,例如SimGAN和CycroGAN。然而,这些方法不会足够敏感地关注科目的形态和形状的各种变化,特别是当培训数据不平衡时,例如培训数据中的某些特殊形状和形状是次要的。为了克服这一问题,我们提议在CycroGAN培训中引入主题的标签信息,例如,对象的形状和类型,并引导它获得符合标签要求的转换模型。我们通过对从SVHN到MNIST的数码图像转换以及从模拟到真实图像的监视相机图像转换进行实验,评估我们提议的称为Label-CycleGAN的方法。我们评估了我们提议的名为Label-CleGAN的方法。