Efficient few-shot machine learning for classification of EBSD patterns
Abstract Deep learning is quickly becoming a standard approach to solving a range of materials science objectives, particularly in the field of computer vision. However, labeled datasets large enough to train neural networks from scratch can be challenging to collect. One approach to accelerating th...
Guardado en:
Autores principales: | Kevin Kaufmann, Hobson Lane, Xiao Liu, Kenneth S. Vecchio |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/b1148711719344a495d8dd02d6386f09 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Few-shot cotton leaf spots disease classification based on metric learning
por: Xihuizi Liang
Publicado: (2021) -
Rapid and flexible segmentation of electron microscopy data using few-shot machine learning
por: Sarah Akers, et al.
Publicado: (2021) -
Few-shot pulse wave contour classification based on multi-scale feature extraction
por: Peng Lu, et al.
Publicado: (2021) -
Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification
por: Youngjae Lee, et al.
Publicado: (2021) -
Optimizing Few-Shot Learning Based on Variational Autoencoders
por: Ruoqi Wei, et al.
Publicado: (2021)