Invertible Conditional GAN Revisited: Photo-to-Manga Face Translation with Modern Architectures (Student Abstract)

With the advent of Machine Learning and Deep Learning, businesses can save time, costs, and manual labor, editing visual content. Generative Adversarial Networks can reconstruct images, complete missing parts and make creative changes, which are otherwise impossible with image editing software. Generative Adversarial Networks can generate images from scratch or from a semantic input to automate the content creation process. We can generate photo-realistic images using sketches or semantic images as input which can be used for creating synthetic training data for visual recognition algorithms and for forensic recognition in criminal identification. We propose the use of conditional GAN's to generate photorealistic images using sketches or Semantic images as input. Our application will focus on generation of city scape photograph, bedroom photograph and human face photograph given a semantic or sketch input. We can use this application for creating synthetic training data for training visual recognition algorithms and for forensic recognition in criminal identification by creating photo realistic images for a given sketch or semantic input.