aangelopoulos / im2im-uqLinks
Image-to-image regression with uncertainty quantification in PyTorch. Take any dataset and train a model to regress images to images with rigorous, distribution-free uncertainty quantification.
☆56Updated 2 years ago
Alternatives and similar repositories for im2im-uq
Users that are interested in im2im-uq are comparing it to the libraries listed below
Sorting:
- ☆32Updated 2 years ago
- Official implementation of "Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems" paper.☆24Updated last year
- Code for the paper "Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification" (2020)☆43Updated last year
- Code for Diff-SCM paper☆98Updated 2 years ago
- InverseBench (ICLR 2025 spotlight)☆52Updated this week
- This is official Pytorch implementation of "Uncertainty quantification in medical image segmentation with Normalizing Flows", Raghavendra…☆30Updated 2 years ago
- Modular and intuitive Hypernetworks in Pytorch☆37Updated last year
- Official repository for the Monte Carlo guided Diffusion for Bayesian linear inverse problems paper:☆19Updated 6 months ago
- Official code for "Enabling Uncertainty Estimation in Iterative Neural Networks" (ICML 2024)☆18Updated 11 months ago
- ☆24Updated 4 years ago
- Code to reproduce results from "Invertible generative models for inverse problems: mitigating representation error and dataset bias"☆21Updated 4 years ago
- ☆94Updated 3 years ago
- ☆18Updated last year
- Measuring the robustness of compressive sensing methods (including deep-learning-based ones) for image reconstruction.☆31Updated 3 years ago
- Official implementation of Learning Diffusion Priors from Observations by Expectation Maximization☆25Updated 3 months ago
- UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs☆11Updated 2 years ago
- Official implementation of ReSample (https://arxiv.org/abs/2307.08123)☆69Updated 7 months ago
- Official implementation of the NeurIPS 2023 paper: "Uncertainty Quantification via Neural Posterior Principal Components"☆11Updated last year
- This is the official implementation of "DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models" (NeurIPS 2024).☆55Updated 8 months ago
- ☆90Updated 2 years ago
- ☆13Updated last year
- A customized PyTorch layer and a customized PyTorch activation function using B-spline transformation☆28Updated 4 years ago
- ☆15Updated 2 years ago
- ☆23Updated 2 years ago
- [ICLR 2025] Official Implementation: "Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models trained on Cor…☆18Updated 3 months ago
- Score-Based Priors for Bayesian Inverse Imaging (ICCV 2023, TMLR 2024)☆34Updated 6 months ago
- Official Code for "Invert to Learn to Invert" that allows training of invertible networks without storing activations☆37Updated 5 years ago
- ☆16Updated 3 years ago
- Official implementation of the paper "Solving Inverse Problems With Deep Neural Networks - Robustness Included?" by M. Genzel, J. Macdona…☆29Updated 3 years ago
- Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains☆48Updated 5 years ago