قارن الطرق

	شبكة عصبونية التفافية ضعيفة الإشراف	شبكة عصبية التفافية شبه مُشرف عليها
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2015–2016	2013–2017
صاحب الطريقة	Oquab, M. et al.; Zhou, B. et al.	Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others)
النوع	Weakly supervised deep learning	Semi-supervised deep learning
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.	Lee, D.-H. (2013). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. ICML Workshop on Challenges in Representation Learning.
الأسماء البديلة	WS-CNN, weakly supervised CNN, CNN with weak labels, CNN with noisy labels	SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN
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.	A Semi-supervised CNN trains a convolutional network on a small labeled image set and a larger pool of unlabeled images simultaneously, using techniques such as pseudo-labeling and consistency regularization to extract supervisory signal from unlabeled data. This strategy closes much of the performance gap caused by scarce annotations without requiring additional human labeling effort.