A group of researchers from MIT Computer Science & Artificial Intelligence Laboratory (CSAIL) have proposed a simple framework for performing different image reconstruction tasks using the state-of-the-art generative model StyleGAN2. It's common for machine learning researchers to train models in a supervised setting for solving downstream prediction and image reconstruction tasks. For example, in the task of super-resolution, which aims to obtain high-resolution output images from low-resolution versions, classical methods train models on pairs of low-resolution and high-resolution images. However, such end-to-end methods can also require re-training whenever there is a distribution shift in the inputs or relevant latent variables. Distribution shifts can easily occur for example in the input x-ray images collected from a hospital if the hospital's medical scanners are upgraded, or as the patients contributing the images age due to improved healthcare. Given the prohibitively high computation resources required to re-train end-to-end approaches when distribution shifts occur, how else might researchers build ML models that are both easy to train and robust to distribution shifts?