مقایسهٔ روش‌ها

	شبکه عصبی کانولوشنی با نظارت ضعیف	شبکه عصبی کانولوشنی نیمه‌نظارت‌شده
حوزه	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning
سال پیدایش	2015–2016	2013–2017
پدیدآور	Oquab, M. et al.; Zhou, B. et al.	Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others)
نوع	Weakly supervised deep learning	Semi-supervised deep learning
منبع بنیادین	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.
نام‌های دیگر	WS-CNN, weakly supervised CNN, CNN with weak labels, CNN with noisy labels	SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN
مرتبط	5	5
خلاصه	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.