6 papers
Domain Adaptation of Mismatched Proximal Denoiser for Plug-and-Play Image Reconstruction
Guixian Xu, Jinglai Li, Junqi Tang
Plug-and-play proximal gradient descent (PnP-PGD) enables flexible image reconstruction by using denoisers as implicit priors. In practice, these denoisers are often deployed outsi…
A New Convergence Analysis of Plug-and-Play Proximal Gradient Descent Under Prior Mismatch
Guixian Xu, Jinglai Li, Junqi Tang
In this work, we provide a new convergence theory for plug-and-play proximal gradient descent (PnP-PGD) under prior mismatch where the denoiser is trained on a different data distr…
Adapting Neural Posterior Estimators for Simulation-Based Inference in Agent-Based Models
Junyao Zhang, Jinglai Li, Junqi Tang
Agent-based models (ABMs) are widely used in economics and social sciences. They represent heterogeneous personal behaviors and their interactions by using simulations, whereas the…
Fast Equivariant Imaging: Accelerating Unsupervised Learning and Model Adaptation via Inexact Splitting
Guixian Xu, Jinglai Li, Junqi Tang
In this work, we propose Fast Equivariant Imaging (FEI), a novel unsupervised learning framework to rapidly and efficiently train deep imaging networks without ground-truth data. F…
Group Symmetry Enables Faster Optimization in Inverse Problems
Junqi Tang, Guixian Xu
We prove for the first time that, if a linear inverse problem exhibits a group symmetry structure, gradient-based optimizers can be designed to exploit this structure for faster co…
Equivariant Test-Time Training with Operator Sketching for Imaging Inverse Problems
Guixian Xu, Jinglai Li, Junqi Tang
Equivariant Imaging (EI) regularization has become the de-facto technique for unsupervised training of deep imaging networks, without any need of ground-truth data. Observing that…