Deep ConvNet: Non-Random Weight Initialization for Repeatable Determinism, Examined with FSGM †

A repeatable and deterministic non-random weight initialization method in convolutional layers of neural networks examined with the Fast Gradient Sign Method (FSGM). Using the FSGM approach as a technique to measure the initialization effect with controlled distortions in transferred learning, varyi...

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Detalles Bibliográficos
Autores principales: Rudd-Orthner, Richard N. M., Mihaylova, Lyudmila
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309697/
https://www.ncbi.nlm.nih.gov/pubmed/34300512
http://dx.doi.org/10.3390/s21144772

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309697/
https://www.ncbi.nlm.nih.gov/pubmed/34300512
http://dx.doi.org/10.3390/s21144772

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