In their seminal work, "Image Style Transfer Using Convolutional Neural Networks," Gatys et al.[R1] demonstrate the efficacy of CNNs in separating and re-combining image content and style to create composite artistic images. Using feature extractions from intermediate layers of a pre-trained CNN, they define separate content and style loss functions, and pose the style transfer task as an optimization problem. We start from a random image and update the pixel values, such that the individual loss functions are minimized. For more details, please refer to this article. One obvious caveat of this approach is that it is slow.