Methoden vergleichen

	Domain-Adaptive Vision Transformer	Domänenadaptives Convolutional Neural Network
Fachgebiet	Deep Learning	Deep Learning
Familie	Machine learning	Machine learning
Entstehungsjahr	2021–2023	2015–2017
Urheber	Multiple groups (Yang et al., 2023; Xu et al., 2021; Ma et al., 2022)	Ganin, Y. & Lempitsky, V. (domain-adversarial framework); Tzeng et al. (ADDA)
Typ	Domain adaptation + Vision Transformer ensemble	Domain-adaptive deep learning model
Wegweisende Quelle	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).	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.
Aliasnamen	DA-ViT, Domain Adaptation with Vision Transformer, ViT with Domain Adaptation, Domain-Adaptive ViT	DA-CNN, domain adaptation CNN, domain-adaptive deep convolutional network, CNN with domain adaptation
Verwandt	5	5
Zusammenfassung	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.	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.