Predicting Hyperparameter Dependencies of Generative Models from Generated Images using Learnable Graph Pooling Network

The core message of this work is to propose a novel Learnable Graph Pooling Network (LGPN) that can effectively capture the dependencies among hyperparameters of generative models (GMs) and improve the performance of model parsing - the task of predicting GM hyperparameters from generated images.