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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2021
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| 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 |