Linganisha mbinu

	Mtandao wa Convolutional unaobadilika kwa Kikoa	Uhamishaji wa Mafunzo kwa Mitandao ya Neura ya Kimkunjo
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2015–2017	2010–2014
Mwanzilishi	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.
Aina	Domain-adaptive deep learning model	Transfer learning applied to convolutional neural networks
Chanzo asilia	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.
Majina mbadala	DA-CNN, domain adaptation CNN, domain-adaptive deep convolutional network, CNN with domain adaptation	TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN
Zinazohusiana	5	4
Muhtasari	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.