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Equivariant Priors for Compressed Sensing with Unknown Orientation. (arXiv:2206.14069v1 [cs.LG])
June 29, 2022, 1:11 a.m. | Anna Kuzina, Kumar Pratik, Fabio Valerio Massoli, Arash Behboodi
stat.ML updates on arXiv.org arxiv.org
In compressed sensing, the goal is to reconstruct the signal from an
underdetermined system of linear measurements. Thus, prior knowledge about the
signal of interest and its structure is required. Additionally, in many
scenarios, the signal has an unknown orientation prior to measurements. To
address such recovery problems, we propose using equivariant generative models
as a prior, which encapsulate orientation information in their latent space.
Thereby, we show that signals with unknown orientations can be recovered with
iterative gradient descent …
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