Deep S$^3$PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models

• URL: http://arxiv.org/abs/2002.05856v2
• Date: Wed, 14 Oct 2020 01:55:29 GMT
• Title: Deep S$^3$PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models
• Authors: Christopher A. Metzler and Gordon Wetzstein
• Abstract summary: This paper introduces and solves the source separation and phase retrieval (S$3$PR) problem. S$3$PR is an important but largely unsolved problem in the application domains, including microscopy, wireless$ communication, and imaging through scattering media.
• Score: 61.508068988778476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces and solves the simultaneous source separation and phase retrieval (S$^3$PR) problem. S$^3$PR is an important but largely unsolved problem in a number application domains, including microscopy, wireless communication, and imaging through scattering media, where one has multiple independent coherent sources whose phase is difficult to measure. In general, S$^3$PR is highly under-determined, non-convex, and difficult to solve. In this work, we demonstrate that by restricting the solutions to lie in the range of a deep generative model, we can constrain the search space sufficiently to solve S$^3$PR.

Related papers

• Multiscale scattered data analysis in samplet coordinates [0.0]
  We study multiscale data scattered schemes for globally supported radial basis functions. We suggest to represent the resulting generalized Vandermonde matrices in samplet coordinates. We prove that the condition numbers of the linear systems at each level remain bounded independent of the particular level.
  arXiv  Detail & Related papers  (2024-09-23T08:07:47Z)
• Polynomial-Time Solutions for ReLU Network Training: A Complexity Classification via Max-Cut and Zonotopes [70.52097560486683]
  We prove that the hardness of approximation of ReLU networks not only mirrors the complexity of the Max-Cut problem but also, in certain special cases, exactly corresponds to it. In particular, when $epsilonleqsqrt84/83-1approx 0.006$, we show that it is NP-hard to find an approximate global dataset of the ReLU network objective with relative error $epsilon$ with respect to the objective value.
  arXiv  Detail & Related papers  (2023-11-18T04:41:07Z)
• Factorization of Multi-Agent Sampling-Based Motion Planning [72.42734061131569]
  Modern robotics often involves multiple embodied agents operating within a shared environment. Standard sampling-based algorithms can be used to search for solutions in the robots' joint space. We integrate the concept of factorization into sampling-based algorithms, which requires only minimal modifications to existing methods. We present a general implementation of a factorized SBA, derive an analytical gain in terms of sample complexity for PRM*, and showcase empirical results for RRG.
  arXiv  Detail & Related papers  (2023-04-01T15:50:18Z)
• DeepHAM: A Global Solution Method for Heterogeneous Agent Models with Aggregate Shocks [9.088303226909277]
  We propose an efficient, reliable, and interpretable global solution method, $textitDeep learning-based algorithm for Heterogeneous Agent Models, DeepHAM$.
  arXiv  Detail & Related papers  (2021-12-29T03:09:19Z)
• Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning [82.31436758872715]
  We develop an algorithm that achieves the same PAC guarantee while using only $O(1)$ episodes of environment interactions. We establish a connection between value functions in discounted and finite-horizon Markov decision processes.
  arXiv  Detail & Related papers  (2021-11-01T00:21:24Z)
• A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance [36.97843660480747]
  The Wasserstein distance has become increasingly important in machine learning and deep learning. A recently proposed approach to alleviate the curse of dimensionality is to project the sampled data onto a lower-dimensional subspace, and then compute the Wasserstein distance between the projected data. However, this approach requires to solve a max-min problem over the Stiefel manifold, which is very challenging in practice. We propose a novel block coordinate descent (RBCD) method to solve this problem, which is based on a novel reformulation of the regularized max-min problem over the Stiefel manifold.
  arXiv  Detail & Related papers  (2020-12-09T17:47:56Z)
• Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses [74.48263583706712]
  3D human pose estimation from a single image is an inverse problem due to the inherent ambiguity of the missing depth. We propose a weakly supervised deep generative network to address the inverse problem.
  arXiv  Detail & Related papers  (2020-08-13T09:26:01Z)
• On the Distribution of Minima in Intrinsic-Metric Rotation Averaging [14.226664427133901]
  This paper studies the spatial distribution of local minima in a Rotation Averaging problem. By recognizing the quotient manifold geometry of the problem we achieve an improvement to general $l_p$ costs. Our results suggest using connectivity as an indicator of problem difficulty.
  arXiv  Detail & Related papers  (2020-03-18T16:03:22Z)
• Joint Wasserstein Distribution Matching [89.86721884036021]
  Joint distribution matching (JDM) problem, which aims to learn bidirectional mappings to match joint distributions of two domains, occurs in many machine learning and computer vision applications. We propose to address JDM problem by minimizing the Wasserstein distance of the joint distributions in two domains. We then propose an important theorem to reduce the intractable problem into a simple optimization problem, and develop a novel method to solve it.
  arXiv  Detail & Related papers  (2020-03-01T03:39:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.