Methoden vergelijken

	Domein-adaptieve Convolutionele Neurale Netwerken	Domein-Adaptieve Vision Transformer
Vakgebied	Deep learning	Deep learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2015–2017	2021–2023
Grondlegger	Ganin, Y. & Lempitsky, V. (domain-adversarial framework); Tzeng et al. (ADDA)	Multiple groups (Yang et al., 2023; Xu et al., 2021; Ma et al., 2022)
Type	Domain-adaptive deep learning model	Domain adaptation + Vision Transformer ensemble
Oorspronkelijke bron	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.	Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations (ICLR).
Aliassen	DA-CNN, domain adaptation CNN, domain-adaptive deep convolutional network, CNN with domain adaptation	DA-ViT, Domain Adaptation with Vision Transformer, ViT with Domain Adaptation, Domain-Adaptive ViT
Verwant	5	5
Samenvatting	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.	Domain-Adaptive Vision Transformer (DA-ViT) applies domain adaptation techniques — such as adversarial alignment, self-training, or attention-level bridging — on top of a pretrained Vision Transformer backbone to transfer visual knowledge from a labeled source domain to an unlabeled or lightly labeled target domain, reducing the distribution shift that limits standard ViT fine-tuning.