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Image-to-Image Regression
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations
Deep Learning as a Method for Inversion of NMR Signals
Reframing the Brain Age Prediction Problem to a More Interpretable and Quantitative Approach
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control
Vehicle Trajectory Prediction on Highways Using Bird Eye View Representations and Deep Learning
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
A deep learning method based on patchwise training for reconstructing temperature field
Deep Capsule Encoder-Decoder Network for Surrogate Modeling and Uncertainty Quantification
Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network
Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification
Joint Deep Reversible Regression Model and Physics-Informed Unsupervised Learning for Temperature Field Reconstruction
Vehicle Trajectory Prediction in Crowded Highway Scenarios Using Bird Eye View Representations and CNNs
Fast Modeling and Understanding Fluid Dynamics Systems with Encoder-Decoder Networks
Fast acoustic scattering using convolutional neural networks
Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression
Evolutionary Neural Architecture Search for Image Restoration
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification
Generalized Deep Image to Image Regression
Predicting the dynamics of 2d objects with a deep residual network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.