Jämför metoder

	Domänadaptiv faltningsnätverk (Convolutional Neural Network, CNN)	Domänadaptivt rekurrent neuralt nätverk
Ämnesområde	Djupinlärning	Djupinlärning
Familj	Machine learning	Machine learning
Ursprungsår	2015–2017	2010s
Upphovsperson	Ganin, Y. & Lempitsky, V. (domain-adversarial framework); Tzeng et al. (ADDA)	Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs)
Typ	Domain-adaptive deep learning model	Domain-adaptive sequential model
Ursprungskälla	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.	Ganin, Y., Ustunova, 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.
Alias	DA-CNN, domain adaptation CNN, domain-adaptive deep convolutional network, CNN with domain adaptation	DA-RNN, domain-adaptive RNN, domain-adapted recurrent network, cross-domain RNN
Närliggande	5	6
Sammanfattning	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.	A Domain-adaptive Recurrent Neural Network (DA-RNN) is a recurrent neural network trained on a source domain and adapted to a target domain using domain adaptation techniques such as adversarial training, feature alignment, or fine-tuning. It enables sequential models to generalise across domains when labeled target-domain data is scarce or unavailable.