Módszerek összehasonlítása

	Finomhangolt konvolúciós neurális hálózat	Transzfer tanulás konvolúciós neurális hálóval
Tudományterület	Mélytanulás	Mélytanulás
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	2012–2014	2010–2014
Megalkotó	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.
Típus	Transfer learning technique (supervised fine-tuning)	Transfer learning applied to convolutional neural networks
Alapmű	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.
Alternatív nevek	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
Kapcsolódó	5	4
Összefoglaló	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.