Bio-inspired zero-shot learning with hierarchical fault-trait knowledge transfer for cross-domain diagnosis.
- DOI
- 10.1016/j.neunet.2026.109702
- Published
- 2027 Feb
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109702,
title = {Bio-inspired zero-shot learning with hierarchical fault-trait knowledge transfer for cross-domain diagnosis.},
author = {Yang Q and Lin Y and Ruan J and Han Y and Xie J and Xue X},
year = {2027},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2026.109702},
url = {https://doi.org/10.1016/j.neunet.2026.109702}
}RIS
TY - JOUR TI - Bio-inspired zero-shot learning with hierarchical fault-trait knowledge transfer for cross-domain diagnosis. AU - Yang Q AU - Lin Y AU - Ruan J AU - Han Y AU - Xie J AU - Xue X PY - 2027 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2026.109702 UR - https://doi.org/10.1016/j.neunet.2026.109702 ER -
APA
Q, Y., Y, L., J, R., Y, H., J, X., & X, X. (2027). Bio-inspired zero-shot learning with hierarchical fault-trait knowledge transfer for cross-domain diagnosis.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2026.109702
Source records
- pubmed · retrieved 2026-11-10T13:16:31.644Z