Task-Aware Compressed Sensing with Generative Adversarial Networks

Understand

In recent years, neural network approaches have been widely adopted for machine learning tasks, with applications in computer vision.

• More recently, unsupervised generative models based on neural networks have been successfully applied to model data distributions via low-dimensional latent spaces.
• In this paper, we use Generative Adversarial Networks (GANs) to impose structure in compressed sensing problems, replacing the usual sparsity constraint.
• We propose to train the GANs in a task-aware fashion, specifically for reconstruction tasks.