1 citations · 3 across the 5 of their papers we have counts for
5 papers
SGD Jittering: A Training Strategy for Robust and Accurate Model-Based Architectures
Peimeng Guan, Mark A. Davenport
Inverse problems aim to reconstruct unseen data from corrupted or perturbed measurements. While most work focuses on improving reconstruction quality, generalization accuracy and r…
MANGO: Learning Disentangled Image Transformation Manifolds with Grouped Operators
Brighton Ancelin, Yenho Chen, Peimeng Guan +4
Learning semantically meaningful image transformations (i.e. rotation, thickness, blur) directly from examples can be a challenging task. Recently, the Manifold Autoencoder (MAE) p…
Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures
Peimeng Guan, Naveed Iqbal, Mark A. Davenport +1
Model-based deep learning methods such as loop unrolling (LU) and deep equilibrium model}(DEQ) extensions offer outstanding performance in solving inverse problems (IP). These meth…
Learned Proximal Operator for Solving Seismic Deconvolution Problem
Peimeng Guan, Naveed Iqbal, Mark A. Davenport +1
Seismic deconvolution is an essential step in seismic data processing that aims to extract layer information from noisy observed traces. In general, this is an ill-posed problem wi…
Loop Unrolled Shallow Equilibrium Regularizer (LUSER) -- A Memory-Efficient Inverse Problem Solver
Peimeng Guan, Jihui Jin, Justin Romberg +1
In inverse problems we aim to reconstruct some underlying signal of interest from potentially corrupted and often ill-posed measurements. Classical optimization-based techniques pr…