Bandingkan metode

	GAN Adaptif Domain	GAN Pembelajaran Transfer
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2016–2017	2014–2018
Pencetus	Ganin et al. (DANN); Zhu et al. (CycleGAN)	Goodfellow, I. et al. (GAN); Wang & Ramanan (transfer to GAN)
Tipe	Generative adversarial model with domain adaptation	Generative model with transferred weights
Sumber perintis	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.	Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems (NeurIPS), 27, 2672–2680.
Alias	DA-GAN, domain adaptation GAN, adversarial domain adaptation, domain-adaptive generative adversarial network	TL-GAN, pretrained GAN, GAN fine-tuning, domain-adaptive GAN
Terkait	6	6
Ringkasan	A Domain-Adaptive GAN combines generative adversarial learning with domain adaptation to bridge the distribution gap between a labeled source domain and an unlabeled or sparsely labeled target domain. By training a generator and discriminator adversarially, the model learns domain-invariant representations or translated samples, enabling a classifier or detector trained on source data to generalize effectively to the target domain without requiring abundant target labels.	Transfer Learning GAN initialises a Generative Adversarial Network — or both its generator and discriminator — from weights pretrained on a large source dataset, then fine-tunes the network on a smaller target dataset. This approach allows high-quality generative modelling even when target-domain data are scarce, by reusing low- and mid-level feature representations learned at scale.