Papers › Misspecified Phase Retrieval with Generative Priors
Misspecified Phase Retrieval with Generative Priors
Zhaoqiang Liu, Xinshao Wang, Jiulong Liu
In this paper, we study phase retrieval under model misspecification and generative priors. In particular, we aim to estimate an n-dimensional signal 𝐱 from m i.i.d.~realizations of the single index model y = f(𝐚ᵀ𝐱), where f is an unknown and possibly random nonlinear link function and 𝐚 ∈ℝⁿ is a standard Gaussian vector. We make the assumption Cov[y,(𝐚ᵀ𝐱)²] 0, which corresponds to the misspecified phase retrieval problem. In addition, the underlying signal 𝐱 is assumed to lie in the range of an L-Lipschitz continuous generative model with bounded k-dimensional inputs. We propose a two-step approach, for which the first step plays the role of spectral initialization and the second step refines the estimated vector produced by the first step iteratively. We show that both steps enjoy a statistical rate of order √((klogL)·(logm)/m) under suitable conditions. Experiments on image datasets are performed to demonstrate that our approach performs on par with or even significantly outperforms several competing methods.
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