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Fine-Tuned Convolutional Neural Network

Menyempurnakan CNN bermaksud memulakan dengan rangkaian yang telah dilatih pada set data besar — lazimnya ImageNet — dan meneruskan latihan pada set data sasaran yang lebih kecil supaya model menyesuaikan ciri visual yang dipelajarinya kepada tugasan baharu. Pendekatan ini mengurangkan secara mendadak data dan pengkomputeran yang diperlukan untuk mencapai prestasi yang kukuh berbanding latihan dari awal.

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Sumber

  1. Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27. link
  2. Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medical Imaging, 35(5), 1299–1312. DOI: 10.1109/TMI.2016.2535302

Cara memetik halaman ini

ScholarGate. (2026, June 3). Fine-Tuned Convolutional Neural Network (CNN Fine-Tuning via Transfer Learning). ScholarGate. https://scholargate.app/ms/deep-learning/fine-tuned-convolutional-neural-network

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ScholarGateFine-Tuned Convolutional Neural Network (Fine-Tuned Convolutional Neural Network (CNN Fine-Tuning via Transfer Learning)). Dicapai 2026-06-15 daripada https://scholargate.app/ms/deep-learning/fine-tuned-convolutional-neural-network · Set data: https://doi.org/10.5281/zenodo.20539026