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. 2002.05692
  4. Cited By
Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE

Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE

13 February 2020
Petru-Daniel Tudosiu
Thomas Varsavsky
Richard Shaw
M. Graham
P. Nachev
Sebastien Ourselin
Carole H. Sudre
M. Jorge Cardoso
    MedIm
ArXivPDFHTML

Papers citing "Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE"

3 / 3 papers shown
Title
MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete Diffusion
MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete Diffusion
Onkar Susladkar
Jishu Sen Gupta
Chirag Sehgal
Sparsh Mittal
Rekha Singhal
DiffM
VGen
37
0
0
10 Oct 2024
Bayesian Image Reconstruction using Deep Generative Models
Bayesian Image Reconstruction using Deep Generative Models
Razvan V. Marinescu
Daniel Moyer
Polina Golland
OOD
DiffM
21
39
0
08 Dec 2020
Real-Time Single Image and Video Super-Resolution Using an Efficient
  Sub-Pixel Convolutional Neural Network
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Wenzhe Shi
Jose Caballero
Ferenc Huszár
J. Totz
Andrew P. Aitken
Rob Bishop
Daniel Rueckert
Zehan Wang
SupR
190
5,173
0
16 Sep 2016
1