关键词: 图像生成, CycleGAN, 特征损失, 图像清晰度, 风格迁移

Abstract:

In the field of image generation, traditional image style transfer requires conversion between two paired images. CycleGAN（Cycle Generative Adversarial Network） has achieved good results by virtue of its ability to generate images for unpaired images, has become a research hotspot in the field of image generation. However, the classical CycleGAN generator cannot accurately identify the specific transformation domain and irrelevant domain of the image. Thus, it has the shortcoming of the arbitrary transformation of image irrelevant domain features, and the distortion of the generated image. For the above questions, identity loss is introduced to constrain the feature recognition of generator, L1 loss is used to guarantee that the converted image corresponds to the pixel level of the original image, effectively improve the problem, and make the generated image clearer. Meanwhile, by adjusting the ratio of identity loss [μ], the identity loss under different [μ] values is further analyzed, and the change of the loss of  CycleGAN and its effect on the quality of the generated image are discuessed. Finally, the selection strategy of proportional superparameter [μ] value of identity loss is given.

Key words: image generation, CycleGAN, identity loss, image sharpness, style transfer