Cargando…
AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19
In recent years, deep learning techniques have been widely used to diagnose diseases. However, in some tasks, such as the diagnosis of COVID-19 disease, due to insufficient data, the model is not properly trained and as a result, the generalizability of the model decreases. For example, if the model...
Autores principales: | Alhares, Hadi, Tanha, Jafar, Balafar, Mohammad Ali |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838404/ http://dx.doi.org/10.1007/s12530-023-09484-2 |
Ejemplares similares
-
Quantum Adversarial Transfer Learning
por: Wang, Longhan, et al.
Publicado: (2023) -
Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application
por: Guo, Wei, et al.
Publicado: (2023) -
Adversarial Deep Transfer Learning in Fault Diagnosis: Progress, Challenges, and Future Prospects
por: Guo, Yu, et al.
Publicado: (2023) -
Improving the adversarial transferability with relational graphs ensemble adversarial attack
por: Pi, Jiatian, et al.
Publicado: (2023) -
Adversarial Multiscale Feature Learning Framework for Overlapping Chromosome Segmentation
por: Mei, Liye, et al.
Publicado: (2022)