Style Transfer via CycleGAN

In machine learning, the study of generative adversarial networks (GANs) is currently one of the most exciting topics. In brief, in GANs, the generator and thediscriminator compete in a game with each other with the goal of generating new data that can pass for real data.In this project, we will explore two types of GANs - Deep Convolutional GAN (DCGAN) and CycleGAN, with more emphasis on the latter. DCGAN is used to generate emojis and CycleGAN is used to transform the style of one image to another. The results show that the cycle consistency losshas improved the results of the CycleGAN model. In the case of DCGAN, mode collapse was observed.

Read full text