Pix2Pix Hyperparameter Optimisation Prediction

Hyperparameter tuning is an important aspect in machine-learning especially for deep generative models. Tuning models to stabilize training and to get the best accuracy can be a time consuming and protracted process. Generative models have a large search space requiring resources and knowledge to find the best parameters. Therefore, in most cases the search space is reduced and parameters are limited to a selected few to save time and computation time. This paper explores three different strategies to predict high impact hyperparameters for Pix2Pix. The achieved results show, that binary classification and regression achieve good results and reliably predict good hyperparameter combinations.