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Subsampling a signal of interest can reduce costly data transfer, battery drain, radiation exposure and acquisition time in a wide range of problems. The recently proposed Deep Probabilistic Subsampling (DPS) method effectively integrates subsampling in an end-to-end deep learning model, but learns a static pattern for all datapoints. We generalize DPS to a sequential method that actively picks the next sample based on the information acquired so far; dubbed Active-DPS (A-DPS). We validate that A-DPS improves over DPS for MNIST classification at high subsampling rates. Moreover, we demonstrate strong performance in active acquisition Magnetic Resonance Image (MRI) reconstruction, outperforming DPS and other deep learning methods.
Author Information
Hans van Gorp (Eindhoven University of Technology)
Iris Huijben (Eindhoven University of Technology)
Bastiaan Veeling (University of Amsterdam)
Nicola Pezzotti (Philips)
Ruud J. G. van Sloun (Technical university of Eindhoven)
Related Events (a corresponding poster, oral, or spotlight)
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2021 Spotlight: Active Deep Probabilistic Subsampling »
Wed. Jul 21st 02:35 -- 02:40 PM Room
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