ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2009.11080
  4. Cited By
GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution
  from Low-Resolution Functional Brain Connectomes

GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes

23 September 2020
Megi Isallari
I. Rekik
ArXivPDFHTML

Papers citing "GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes"

3 / 3 papers shown
Title
Optimization of Module Transferability in Single Image Super-Resolution: Universality Assessment and Cycle Residual Blocks
Optimization of Module Transferability in Single Image Super-Resolution: Universality Assessment and Cycle Residual Blocks
Haotong Cheng
Zhiqi Zhang
Hao Li
Xiaotian Zhang
SupR
49
0
0
06 May 2025
Deep Cross-Modality and Resolution Graph Integration for Universal Brain
  Connectivity Mapping and Augmentation
Deep Cross-Modality and Resolution Graph Integration for Universal Brain Connectivity Mapping and Augmentation
Ece Cinar
Sinem Elif Haseki
Alaa Bessadok
I. Rekik
21
2
0
13 Sep 2022
Inter-Domain Alignment for Predicting High-Resolution Brain Networks
  Using Teacher-Student Learning
Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning
Basar Demir
Alaa Bessadok
I. Rekik
28
0
0
06 Oct 2021
1