Salīdzināt metodes

	Adaptīvs domēna GAN	Pielāgotā ģeneratīvā pretestības tīkls
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2016–2017	2014 (GAN); 2019–2020 (fine-tuning paradigm)
Autors	Ganin et al. (DANN); Zhu et al. (CycleGAN)	Goodfellow, I. et al. (GAN); fine-tuning practice established ~2019–2020
Tips	Generative adversarial model with domain adaptation	Generative model (adversarial training + transfer)
Pirmavots	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.
Citi nosaukumi	DA-GAN, domain adaptation GAN, adversarial domain adaptation, domain-adaptive generative adversarial network	Fine-Tuned GAN, GAN Fine-Tuning, Domain-Adapted GAN, Transfer GAN
Saistītās	6	6
Kopsavilkums	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.	A Fine-Tuned GAN starts from a large pre-trained generative adversarial network and continues adversarial training on a smaller target dataset, allowing the model to synthesize high-quality samples in a new domain without training from scratch. This transfer approach dramatically reduces data and compute requirements while preserving the rich feature representations learned during pre-training.