Oral
Deep Compressed Sensing
Yan Wu · Mihaela Rosca · Timothy Lillicrap

Wed Jun 12th 04:30 -- 04:35 PM @ Hall A

Compressed sensing (CS) provides an elegant framework for recovering sparse signals from compressed measurements. For example, CS can exploit the structure of natural images and recover an image from only a few random measurements. CS is highly flexible and data efficient, but its application has been restricted by the strong assumption of sparsity and costly optimisation process. A recent approach that combines CS with neural network generators has removed the constraint of sparsity, but reconstruction remains slow. Here we propose a novel framework that significantly improves both the performance and speed of signal recovery by jointly training a generator and the optimisation process for reconstruction via meta-learning. We explore training the measurements with different objectives, and derive a family of models based on minimising measurement errors. We show that Generative Adversarial Nets (GANs) can be viewed as a special case in this family of models. Borrowing insights from the CS perspective, we develop a novel way of stabilising GAN training using gradient information from the discriminator.

Author Information

Yan Wu (DeepMind)
Mihaela Rosca (DeepMind)
Tim Lillicrap (Google DeepMind)

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