@article {5517,
	title = {Top-tuning: A study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods},
	journal = {Image and Vision Computing},
	volume = {142},
	year = {2024},
	month = {02/2024},
	pages = {104894},
	abstract = {<p>The impressive performance of deep learning architectures is associated with a massive increase in model complexity. Millions of parameters need to be tuned, with training and inference time scaling accordingly, together with energy consumption. But is massive fine-tuning always necessary? In this paper, focusing on image classification, we consider a simple transfer learning approach exploiting pre-trained convolutional features as input for a fast-to-train kernel method. We refer to this approach as top-tuning since only the kernel classifier is trained on the target dataset. In our study, we perform more than 3000 training processes focusing on 32 small to medium-sized target datasets, a typical situation where transfer learning is necessary. We show that the top-tuning approach provides comparable accuracy with respect to fine-tuning, with a training time between one and two orders of magnitude smaller. These results suggest that top-tuning is an effective alternative to fine-tuning in small/medium datasets, being especially useful when training time efficiency and computational resources saving are crucial.</p>
},
	issn = {02628856},
	doi = {10.1016/j.imavis.2023.104894},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0262885623002688},
	author = {Alfano, Paolo Didier and Pastore, Vito Paolo and Rosasco, Lorenzo and Odone, Francesca}
}
