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Generative adversarial network based synthetic data training model for lightweight convolutional neural networks
Inadequate training data is a significant challenge for deep learning techniques, particularly in applications where data is difficult to get, and publicly available datasets are uncommon owing to ethical and privacy concerns. Various approaches, such as data augmentation and transfer learning, are...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer US
2023
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199442/ https://www.ncbi.nlm.nih.gov/pubmed/37362646 http://dx.doi.org/10.1007/s11042-023-15747-6 |