In this project, we utilized a modified GAN called CycleGAN to perform collection style transfer, where an image is re-drawn in the style of a particular artist based on a set of input images. Additionally, we employed a CNN to quantitatively assess the style of images generated by CycleGAN, comparing them to a baseline image. Our results demonstrate that the CNN can successfully distinguish between images created by a specific artist and random images, allowing it to objectively measure the style of images produced by CycleGAN. Furthermore, we show that our CycleGAN outperforms the pre-trained CycleGAN provided by its original creators on certain datasets.
