Papers with Code 아카이브

Image-to-Image Regression

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://codeberg.org/fuchsfa/foundation-models-seismic-processing

공식 코드

Foundation Models For Seismic Data Processing: An Extensive Review

https://github.com/Arnukk/CASPIAN

공식 코드

Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations

https://github.com/aangelopoulos/im2im-uq

공식 코드

Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging

https://github.com/cics-nd/dcedn-gcs

공식 코드

Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media

https://github.com/csccm-iitd/glunet

공식 코드

Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification

https://github.com/microsoft/AcousticScatteringData

공식 코드

Fast acoustic scattering using convolutional neural networks

https://github.com/shendu-sw/PIRL

공식 코드

Joint Deep Reversible Regression Model and Physics-Informed Unsupervised Learning for Temperature Field Reconstruction

https://github.com/sulam-group/k-rcps

공식 코드

How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control

https://github.com/xiaohu-zheng/deep-mc-qr

공식 코드

Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction

https://github.com/lightning-uq-box/lightning-uq-box

Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging

https://github.com/venkai/RBDN

Generalized Deep Image to Image Regression

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.