PSRFlow: Probabilistic Super Resolution with Flow-Based Models for Scientific Data-Reference-Cited by-同舟云学术

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PSRFlow: Probabilistic Super Resolution with Flow-Based Models for Scientific Data

Published:2023 Issue: Volume: Page:1-11
ISSN:1077-2626
Container-title:IEEE Transactions on Visualization and Computer Graphics
language:
Short-container-title:IEEE Trans. Visual. Comput. Graphics

Author:

Shen Jingyi¹^ORCID,Shen Han-Wei¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering, The Ohio State University, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

http://xplorestaging.ieee.org/ielx7/2945/4359476/10309865.pdf?arnumber=10309865

Reference42 articles.

1. Using Vaes and Normalizing Flows for One-Shot Text-To-Speech Synthesis of Expressive Speech

2. STSRNet: Deep Joint Space–Time Super-Resolution for Vector Field Visualization

3. Analyzing inverse Problems with Invertible Neural Networks;Ardizzone;arXiv preprint arXiv,2018

4. Towards Principled Methods for Training Generative Adversarial Networks;Arjovsky,2017

5. Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models

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