The study of object transformation based on CycleGAN

Abstract

At present, generative adaptive networks (GAN), has become a popular module in deep learning. GAN is very effective in image generation and image style migration. At present, the research on the migration of image style is focused on images such as oil painting and landscape painting, and lacks the research on the conversion between object images. This paper extends the style transfer technology to object recognition, uses CycleGAN method to learn the mapping relationship between zebra and horse, and realizes the transformation between zebra and horse. Viewing the generation effect of different learning methods by changing the learning times and learning rate policy. This work realizes the conversion between zebra and horse, and shows the generated pictures under different training times and different learning situations. Under the same training times, the conversion effect from horse to zebra will be better. After a certain number of trainings, the training effect will gradually decline. The conversion effect of the same type will be improved with the increase of training times. Different learning rate policies will bring different generation effects.