قارن الطرق

	الشبكة العصبونية الالتفافية المُحسَّنة بدقة	نقل التعلم باستخدام الشبكات العصبية الالتفافية
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2012–2014	2010–2014
صاحب الطريقة	Yosinski, J. et al. (theoretical basis); practice widespread from Krizhevsky et al. 2012 onward	Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al.
النوع	Transfer learning technique (supervised fine-tuning)	Transfer learning applied to convolutional neural networks
المصدر التأسيسي	Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
الأسماء البديلة	Fine-tuned CNN, CNN fine-tuning, CNN transfer learning with fine-tuning, adapted convolutional network	TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN
ذات صلة	5	4
الملخص	Fine-tuning a CNN means starting from a network already trained on a large dataset — typically ImageNet — and continuing training on a smaller target dataset so the model adapts its learned visual features to a new task. This approach dramatically reduces the data and compute required to reach strong performance compared with training from scratch.	Transfer Learning with CNN reuses a convolutional neural network that has already been trained on a large dataset — most commonly ImageNet — and adapts its learned feature detectors to a new, often smaller target dataset. This lets researchers achieve strong image-recognition performance without the massive compute and data resources required to train a CNN from scratch.