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Deep Learning of Crystalline Defects from TEM images: A Solution for the
  Problem of "Never Enough Training Data"

Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data"

12 July 2023
Kishan Govind
D. Oliveros
A. Dlouhý
M. Legros
Stefan Sandfeld
ArXivPDFHTML

Papers citing "Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data""

4 / 4 papers shown
Title
Self-Supervised Learning with Generative Adversarial Networks for
  Electron Microscopy
Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy
Bashir Kazimi
Karina Ruzaeva
Stefan Sandfeld
30
4
0
28 Feb 2024
Combining unsupervised and supervised learning in microscopy enables
  defect analysis of a full 4H-SiC wafer
Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer
Binh Duong Nguyen
Johannes Steiner
Peter Wellmann
Stefan Sandfeld
27
0
0
20 Feb 2024
Modeling Dislocation Dynamics Data Using Semantic Web Technologies
Modeling Dislocation Dynamics Data Using Semantic Web Technologies
Ahmad Zainul Ihsan
Said Fathalla
Stefan Sandfeld
AI4CE
11
0
0
13 Sep 2023
Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance
Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance
Qinqin Yang
Zi Wang
Kunyuan Guo
C. Cai
X. Qu
PINN
OOD
MedIm
AI4CE
27
56
0
21 Mar 2022
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