Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Blind Deconvolution via Mirror Descent   We revisit the Blind Deconvolution problem with a focus on understanding its
robustness and convergence properties. Provable robustness to noise and other
perturbations is receiving recent interest in vision, from obtaining immunity
to adversarial attacks to assessing and describing failure modes of algorithms
in mission critical applications. Further, many blind deconvolution methods
based on deep architectures internally make use of or optimize the basic
formulation, so a clearer understanding of how this sub-module behaves, when it
can be solved, and what noise injection it can tolerate is a first order
requirement. We derive new insights into the theoretical underpinnings of blind
deconvolution. The algorithm that emerges has nice convergence guarantees and
is provably robust in a sense we formalize in the paper. Interestingly, these
technical results play out very well in practice, where on standard datasets
our algorithm yields results competitive with or superior to the state of the
art. Keywords: blind deconvolution, robust continuous optimization

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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: math
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The categories of this paper are: math-ph
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-alg