Cargando…
Robust adversarial uncertainty quantification for deep learning fine-tuning
This paper proposes a deep learning model that is robust and capable of handling highly uncertain inputs. The model is divided into three phases: creating a dataset, creating a neural network based on the dataset, and retraining the neural network to handle unpredictable inputs. The model utilizes e...
| Autores principales: | , |
|---|---|
| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer US
2023
|
| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9957691/ https://www.ncbi.nlm.nih.gov/pubmed/37206086 http://dx.doi.org/10.1007/s11227-023-05087-5 |