Compară metode

	Clasificarea imaginilor prin ajustare fină (fine-tuning)	Rețea neuronală convoluțională (CNN) ajustată fin (Fine-Tuned)
Domeniu	Învățare profundă	Învățare profundă
Familie	Machine learning	Machine learning
Anul apariției	2010–2014	2012–2014
Autorul original	Yosinski, J. et al.; Pan, S. J. & Yang, Q.	Yosinski, J. et al. (theoretical basis); practice widespread from Krizhevsky et al. 2012 onward
Tip	Transfer learning / fine-tuning	Transfer learning technique (supervised fine-tuning)
Sursa seminală	Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems (NeurIPS), 27, 3320–3328.	Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27.
Denumiri alternative	fine-tuning for image recognition, transfer learning image classifier, pretrained CNN fine-tuning, domain-specific image classifier	Fine-tuned CNN, CNN fine-tuning, CNN transfer learning with fine-tuning, adapted convolutional network
Înrudite	5	5
Rezumat	Fine-tuned image classification adapts a large neural network pretrained on a broad image corpus (such as ImageNet) to a specific target domain by continuing training on labeled domain images. This approach achieves strong accuracy with far fewer target-domain samples than training from scratch, making it the dominant paradigm for applied computer vision tasks.	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.