Deep probabilistic subsampling for task-adaptive compressed sensing
Iris A. M. Huijben, Bastiaan S. Veeling, Ruud J. G. van Sloun
Abstract
The field of deep learning is commonly concerned with optimizing predictive models using large pre-acquired datasets of densely sampled datapoints or signals. In this work, we demonstrate that the deep learning paradigm can be extended to incorporate a subsampling scheme that is jointly optimized under a desired sampling rate. We present Deep Probabilistic Subsampling (DPS), a widely applicable framework for task-adaptive compressed sensing that enables end-to-end optimization of an optimal subset of signal samples with a subsequent model that performs a required task. We demonstrate strong performance on reconstruction and classification tasks of a toy dataset, MNIST, and CIFAR10 under stringent subsampling rates in both the pixel and the spatial frequency domain. Thanks to the data-driven nature of the framework, DPS is directly applicable to all real-world domains that benefit from sample rate reduction. The code used for this paper is made publicly available.