Linganisha mbinu

	Uhamishaji wa Mafunzo kwa Mitandao ya Neura ya Kimkunjo	Convolutional Neural Network Iliyoendeshwa kwa Kina
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2010–2014	2012–2014
Mwanzilishi	Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al.	Yosinski, J. et al. (theoretical basis); practice widespread from Krizhevsky et al. 2012 onward
Aina	Transfer learning applied to convolutional neural networks	Transfer learning technique (supervised fine-tuning)
Chanzo asilia	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27.
Majina mbadala	TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN	Fine-tuned CNN, CNN fine-tuning, CNN transfer learning with fine-tuning, adapted convolutional network
Zinazohusiana	4	5
Muhtasari	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.	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.