372 research outputs found
Theoretical Perspectives on Deep Learning Methods in Inverse Problems
In recent years, there have been significant advances
in the use of deep learning methods in inverse problems such as
denoising, compressive sensing, inpainting, and super-resolution.
While this line of works has predominantly been driven by
practical algorithms and experiments, it has also given rise to
a variety of intriguing theoretical problems. In this paper, we
survey some of the prominent theoretical developments in this line
of works, focusing in particular on generative priors, untrained
neural network priors, and unfolding algorithms. In addition to
summarizing existing results in these topics, we highlight several
ongoing challenges and open problems
Inferring Rankings Using Constrained Sensing
We consider the problem of recovering a function over the space of
permutations (or, the symmetric group) over elements from given partial
information; the partial information we consider is related to the group
theoretic Fourier Transform of the function. This problem naturally arises in
several settings such as ranked elections, multi-object tracking, ranking
systems, and recommendation systems. Inspired by the work of Donoho and Stark
in the context of discrete-time functions, we focus on non-negative functions
with a sparse support (support size domain size). Our recovery method is
based on finding the sparsest solution (through optimization) that is
consistent with the available information. As the main result, we derive
sufficient conditions for functions that can be recovered exactly from partial
information through optimization. Under a natural random model for the
generation of functions, we quantify the recoverability conditions by deriving
bounds on the sparsity (support size) for which the function satisfies the
sufficient conditions with a high probability as .
optimization is computationally hard. Therefore, the popular compressive
sensing literature considers solving the convex relaxation,
optimization, to find the sparsest solution. However, we show that
optimization fails to recover a function (even with constant sparsity)
generated using the random model with a high probability as . In
order to overcome this problem, we propose a novel iterative algorithm for the
recovery of functions that satisfy the sufficient conditions. Finally, using an
Information Theoretic framework, we study necessary conditions for exact
recovery to be possible.Comment: 19 page
Information-Driven Adaptive Structured-Light Scanners
Sensor planning and active sensing, long studied in robotics, adapt sensor parameters to maximize a utility function while constraining resource expenditures. Here we consider information gain as the utility function. While these concepts are often used to reason about 3D sensors, these are usually treated as a predefined, black-box, component. In this paper we show how the same principles can be used as part of the 3D sensor. We describe the relevant generative model for structured-light 3D scanning and show how adaptive pattern selection can maximize information gain in an open-loop-feedback manner. We then demonstrate how different choices of relevant variable sets (corresponding to the subproblems of locatization and mapping) lead to different criteria for pattern selection and can be computed in an online fashion. We show results for both subproblems with several pattern dictionary choices and demonstrate their usefulness for pose estimation and depth acquisition.United States. Office of Naval Research (Grant N00014-09-1-1051)United States. Army Research Office (Grant W911NF-11- 1-0391)United States. Office of Naval Research (Grant N00014- 11-1-0688
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