Image-to-Image Regression
10 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Image-to-Image Regression
2007 BP Anisotropic Velocity Benchmark
Most implemented papers
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
Image-to-image regression is an important learning task, used frequently in biological imaging.
Generalized Deep Image to Image Regression
We present a Deep Convolutional Neural Network architecture which serves as a generic image-to-image regressor that can be trained end-to-end without any further machinery.
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
A training strategy combining a regression loss and a segmentation loss is proposed in order to better approximate the discontinuous saturation field.
Fast acoustic scattering using convolutional neural networks
Diffracted scattering and occlusion are important acoustic effects in interactive auralization and noise control applications, typically requiring expensive numerical simulation.
Joint Deep Reversible Regression Model and Physics-Informed Unsupervised Learning for Temperature Field Reconstruction
shendu-sw/PIRL
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To solve this problem, this work develops a novel physics-informed deep reversible regression models for temperature field reconstruction of heat-source systems (TFR-HSS), which can better reconstruct the temperature field with limited monitoring points unsupervisedly.
Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification
The proposed GLU-net treats the uncertainty propagation problem as an image to image regression and hence, is extremely data efficient.
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
However, a lot of labeled data is needed to train CNN, and the common CNN can not quantify the aleatoric uncertainty caused by data noise.
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control
Score-based generative modeling, informally referred to as diffusion models, continue to grow in popularity across several important domains and tasks.
Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations
Arnukk/CASPIAN
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In light of growing threats posed by climate change in general and sea level rise (SLR) in particular, the necessity for computationally efficient means to estimate and analyze potential coastal flood hazards has become increasingly pressing.
Foundation Models For Seismic Data Processing: An Extensive Review
Seismic processing plays a crucial role in transforming raw data into high-quality subsurface images, pivotal for various geoscience applications.
