قارن الطرق

	شبكة عصبونية التفافية ضعيفة الإشراف	الشبكة العصبونية الالتفافية المُحسَّنة بدقة
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2015–2016	2012–2014
صاحب الطريقة	Oquab, M. et al.; Zhou, B. et al.	Yosinski, J. et al. (theoretical basis); practice widespread from Krizhevsky et al. 2012 onward
النوع	Weakly supervised deep learning	Transfer learning technique (supervised fine-tuning)
المصدر التأسيسي	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.
الأسماء البديلة	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
ذات صلة	5	5
الملخص	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.