Σύγκριση μεθόδων

	Συνελικτικό Νευρωνικό Δίκτυο Προσαρμοσμένο στον Τομέα	Εκμάθηση Μεταφοράς με Συνελικτικά Νευρωνικά Δίκτυα
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2015–2017	2010–2014
Δημιουργός	Ganin, Y. & Lempitsky, V. (domain-adversarial framework); Tzeng et al. (ADDA)	Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al.
Τύπος	Domain-adaptive deep learning model	Transfer learning applied to convolutional neural networks
Θεμελιώδης πηγή	Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Εναλλακτικές ονομασίες	DA-CNN, domain adaptation CNN, domain-adaptive deep convolutional network, CNN with domain adaptation	TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN
Συναφείς	5	4
Σύνοψη	A domain-adaptive CNN trains a convolutional network on a labeled source domain and adapts its learned feature representations to an unlabeled or lightly labeled target domain, bridging the distribution gap so that visual classifiers transfer reliably across datasets, sensors, or imaging conditions without full re-annotation.	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.