Confronta i metodi

	Rete Neurale Convoluzionale Debolmente Supervisionata	Convolutional Neural Network Fine-Tuned
Campo	Apprendimento profondo	Apprendimento profondo
Famiglia	Machine learning	Machine learning
Anno di origine	2015–2016	2012–2014
Ideatore	Oquab, M. et al.; Zhou, B. et al.	Yosinski, J. et al. (theoretical basis); practice widespread from Krizhevsky et al. 2012 onward
Tipo	Weakly supervised deep learning	Transfer learning technique (supervised fine-tuning)
Fonte seminale	Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2921–2929.	Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27.
Alias	WS-CNN, weakly supervised CNN, CNN with weak labels, CNN with noisy labels	Fine-tuned CNN, CNN fine-tuning, CNN transfer learning with fine-tuning, adapted convolutional network
Correlati	5	5
Sintesi	A weakly supervised CNN is a convolutional neural network trained with incomplete, coarse, or noisy annotations instead of full pixel-level or bounding-box labels. Typical weak labels include image-level class tags, partial annotations, or crowd-sourced noisy labels. The model learns to classify and often to roughly localize objects using these cheaper, lower-quality supervision signals.	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.