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Image-to-Image Regression

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Showing 1–22 of 22 papers

TitleStatusHype
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk ControlCode1
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in ImagingCode1
Joint Deep Reversible Regression Model and Physics-Informed Unsupervised Learning for Temperature Field ReconstructionCode0
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous mediaCode0
Fast acoustic scattering using convolutional neural networksCode0
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field ReconstructionCode0
Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline AdaptationsCode0
Gated Linear Model induced U-net for surrogate modeling and uncertainty quantificationCode0
Foundation Models For Seismic Data Processing: An Extensive ReviewCode0
Generalized Deep Image to Image RegressionCode0
Vehicle Trajectory Prediction in Crowded Highway Scenarios Using Bird Eye View Representations and CNNs—0
A deep learning method based on patchwise training for reconstructing temperature field—0
Vehicle Trajectory Prediction on Highways Using Bird Eye View Representations and Deep Learning—0
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification—0
Deep Capsule Encoder-Decoder Network for Surrogate Modeling and Uncertainty Quantification—0
Deep Learning as a Method for Inversion of NMR Signals—0
Evolutionary Neural Architecture Search for Image Restoration—0
Fast Modeling and Understanding Fluid Dynamics Systems with Encoder-Decoder Networks—0
Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression—0
Predicting the dynamics of 2d objects with a deep residual network—0
Reframing the Brain Age Prediction Problem to a More Interpretable and Quantitative Approach—0
Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network—0
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