Sammenlign metoder

	Domæne-adaptiv Multilayer Perceptron	Domæne-adaptivt Konvolutionelt Neuralt Netværk
Fagområde	Dyb læring	Dyb læring
Familie	Machine learning	Machine learning
Oprindelsesår	2006–2016	2015–2017
Ophavsperson	Ben-David et al.; Ganin et al.	Ganin, Y. & Lempitsky, V. (domain-adversarial framework); Tzeng et al. (ADDA)
Type	Domain adaptation of feedforward neural network	Domain-adaptive deep learning model
Oprindelig kilde	Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010). A theory of learning from different domains. Machine Learning, 79(1–2), 151–175.	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.
Aliasser	DA-MLP, domain-adaptive MLP, domain-adapted feedforward network, domain adaptation with MLP	DA-CNN, domain adaptation CNN, domain-adaptive deep convolutional network, CNN with domain adaptation
Relaterede	5	5
Resumé	A domain-adaptive multilayer perceptron (DA-MLP) is a feedforward neural network trained to learn representations that are useful across a labeled source domain and an unlabeled or differently distributed target domain. By minimizing both a task loss and a domain-discrepancy objective, the MLP generalizes to the target domain with little or no target-domain labels.	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.