Image-to-Image Regression

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

10 shown of 10 papers with code (22 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
  10 Feb 2022 2 repositories listed Syntology ran 2 of 21 samples · 19 unverified
  Image-to-image regression is an important learning task, used frequently in biological imaging.
• Foundation Models For Seismic Data Processing: An Extensive Review
  31 Mar 2025 1 repository listed
  Seismic processing plays a crucial role in transforming raw data into high-quality subsurface images, pivotal for various geoscience applications.
• Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations
  6 Jun 2024 1 repository listed
  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…
• How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control
  7 Feb 2023 1 repository listed Syntology ran 0 of 6 samples · 6 unverified
  Score-based generative modeling, informally referred to as diffusion models, continue to grow in popularity across several important domains and tasks.
• Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
  14 Feb 2022 1 repository listed
  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.
• Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification
  8 Nov 2021 1 repository listed
  The proposed GLU-net treats the uncertainty propagation problem as an image to image regression and hence, is extremely data efficient.
• Joint Deep Reversible Regression Model and Physics-Informed Unsupervised Learning for Temperature Field Reconstruction
  22 Jun 2021 1 repository listed
  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…
• Fast acoustic scattering using convolutional neural networks
  30 Oct 2019 1 repository listed
  Diffracted scattering and occlusion are important acoustic effects in interactive auralization and noise control applications, typically requiring expensive numerical simulation.
• Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
  2 Jul 2018 1 repository listed
  A training strategy combining a regression loss and a segmentation loss is proposed in order to better approximate the discontinuous saturation field.
• Generalized Deep Image to Image Regression
  10 Dec 2016 1 repository listed
  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.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.