Papers Image-to-Image Regression
“Image-to-Image Regression” 태그가 달린 논문 22편 · 필터 해제
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. Despite its importance, traditional seismic processing techniques face ch…
DenoisingImage-to-Image RegressionDeep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations
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 …
Depth EstimationFlood Inundation MappingImage-to-Image RegressionImage-to-Image Translation+2Deep Learning as a Method for Inversion of NMR Signals
The concept of deep learning is employed for the inversion of NMR signals and it is shown that NMR signal inversion can be considered as an image-to-image regression problem, which can be treated with a convolutional neu…
Deep LearningImage-to-Image RegressionReframing the Brain Age Prediction Problem to a More Interpretable and Quantitative Approach
Deep learning models have achieved state-of-the-art results in estimating brain age, which is an important brain health biomarker, from magnetic resonance (MR) images. However, most of these models only provide a global …
Image-to-Image RegressionPredictionHow 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. While they provide high-quality and diverse samples from empirical d…
Computed Tomography (CT)Conformal PredictionDenoisingImage Denoising+1Vehicle Trajectory Prediction on Highways Using Bird Eye View Representations and Deep Learning
This work presents a novel method for predicting vehicle trajectories in highway scenarios using efficient bird's eye view representations and convolutional neural networks. Vehicle positions, motion histories, road conf…
Image-to-Image RegressionPredictionTrajectory PredictionDeep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
For the temperature field reconstruction (TFR), a complex image-to-image regression problem, the convolutional neural network (CNN) is a powerful surrogate model due to the convolutional layer's good image feature extrac…
Image-to-Image Regressionquantile regressionregressionImage-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. Current algorithms, however, do not generally offer statistical guarantees that protect against a model's mistakes and hallu…
Conformal PredictionImage-to-Image RegressionPrediction Intervalsregression+2A deep learning method based on patchwise training for reconstructing temperature field
Physical field reconstruction is highly desirable for the measurement and control of engineering systems. The reconstruction of the temperature field from limited observation plays a crucial role in thermal management fo…
Image-to-Image RegressionManagementDeep Capsule Encoder-Decoder Network for Surrogate Modeling and Uncertainty Quantification
We propose a novel \textit{capsule} based deep encoder-decoder model for surrogate modeling and uncertainty quantification of systems in mechanics from sparse data. The proposed framework is developed by adapting Capsule…
DecoderImage-to-Image RegressionUncertainty QuantificationUncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network
We build surrogate models for dynamic 3D subsurface single-phase flow problems with multiple vertical producing wells. The surrogate model provides efficient pressure estimation of the entire formation at any timestep gi…
Computational EfficiencyDecoderImage-to-Image RegressionUncertainty QuantificationGated Linear Model induced U-net for surrogate modeling and uncertainty quantification
We propose a novel deep learning based surrogate model for solving high-dimensional uncertainty quantification and uncertainty propagation problems. The proposed deep learning architecture is developed by integrating the…
Image-to-Image RegressionUncertainty QuantificationJoint Deep Reversible Regression Model and Physics-Informed Unsupervised Learning for Temperature Field Reconstruction
Temperature monitoring during the life time of heat source components in engineering systems becomes essential to guarantee the normal work and the working life of these components. However, prior methods, which mainly u…
Image-to-Image RegressionregressionVehicle Trajectory Prediction in Crowded Highway Scenarios Using Bird Eye View Representations and CNNs
This paper describes a novel approach to perform vehicle trajectory predictions employing graphic representations. The vehicles are represented using Gaussian distributions into a Bird Eye View. Then the U-net model is u…
Image-to-Image RegressionTrajectory PredictionFast Modeling and Understanding Fluid Dynamics Systems with Encoder-Decoder Networks
Is a deep learning model capable of understanding systems governed by certain first principle laws by only observing the system's output? Can deep learning learn the underlying physics and honor the physics when making p…
DecoderDeep LearningImage-to-Image RegressionFast 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. We propose training a convolutional neura…
Image-to-Image RegressionregressionForecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression
The concept of mobility prediction represents one of the key enablers for an efficient management of future cellular networks, which tend to be progressively more elaborate and dense due to the aggregation of multiple te…
Image-to-Image RegressionManagementPredictionregression+1Evolutionary Neural Architecture Search for Image Restoration
Convolutional neural network (CNN) architectures have traditionally been explored by human experts in a manual search process that is time-consuming and ineffectively explores the massive space of potential solutions. Ne…
GPUimage-classificationImage ClassificationImage Restoration+2Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
Surrogate strategies are used widely for uncertainty quantification of groundwater models in order to improve computational efficiency. However, their application to dynamic multiphase flow problems is hindered by the cu…
Computational EfficiencyDecoderImage-to-Image Regressionregression+1Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification
We are interested in the development of surrogate models for uncertainty quantification and propagation in problems governed by stochastic PDEs using a deep convolutional encoder-decoder network in a similar fashion to a…
Bayesian InferenceDecoderGaussian ProcessesImage-to-Image Regression+2