Cargando…
Efficient few-shot machine learning for classification of EBSD patterns
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 the trainin...
Autores principales: | Kaufmann, Kevin, Lane, Hobson, Liu, Xiao, Vecchio, Kenneth S. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8046977/ https://www.ncbi.nlm.nih.gov/pubmed/33854109 http://dx.doi.org/10.1038/s41598-021-87557-5 |
Ejemplares similares
-
Insights into few shot learning approaches for image scene classification
por: Soudy, Mohamed, et al.
Publicado: (2021) -
Automated classification of polyps using deep learning architectures and few-shot learning
por: Krenzer, Adrian, et al.
Publicado: (2023) -
FewJoint: few-shot learning for joint dialogue understanding
por: Hou, Yutai, et al.
Publicado: (2022) -
Hybrid Fine-Tuning Strategy for Few-Shot Classification
por: Zhao, Lei, et al.
Publicado: (2022) -
Few-shot cotton leaf spots disease classification based on metric learning
por: Liang, Xihuizi
Publicado: (2021)