Active Subsampling Using Deep Generative Models by Maximizing Expected Information Gain

We introduce an adaptive, fully probabilistic pipeline for optimized signal subsampling in sampling-budget constrained systems. Our pipeline equips an agent with a deep generative model of its measurement-generating environment with which it infers posterior distributions over high-dimensional signals. This posterior distribution is subsequently used by the agent to adaptively select next samples that maximize the expected information gain. Experiments on the MNIST and fastMRI data sets show strong adaptability of selected sampling sequences to the signal modality, resulting in high-quality reconstructions for high acceleration factors. Performance is upper-bounded by the representation error of the used generative model, which is mainly evident at low acceleration factors.