Machine learning practitioners are increasingly turning to the power of generative adversarial networks (GANs) for image processing. Applications that really benefit from using GANs include: generating art and photos from text-based descriptions, upscaling images, transferring images across domains (e.g., changing day time scenes to night time), and many others.
A generative adversarial network (GAN) is a powerful approach to machine learning (ML). At a high level, a GAN is simply two neural networks that feed into each other. One produces increasingly accurate data while the other gradually improves its ability to classify such data. In this blog we'll dive