Σύγκριση μεθόδων

	Ημι-εποπτευόμενο Συνελικτικό Νευρωνικό Δίκτυο	Εκμάθηση Μεταφοράς με Συνελικτικά Νευρωνικά Δίκτυα
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2013–2017	2010–2014
Δημιουργός	Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others)	Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al.
Τύπος	Semi-supervised deep learning	Transfer learning applied to convolutional neural networks
Θεμελιώδης πηγή	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.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Εναλλακτικές ονομασίες	SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN	TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN
Συναφείς	5	4
Σύνοψη	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.	Transfer Learning with CNN reuses a convolutional neural network that has already been trained on a large dataset — most commonly ImageNet — and adapts its learned feature detectors to a new, often smaller target dataset. This lets researchers achieve strong image-recognition performance without the massive compute and data resources required to train a CNN from scratch.