Сравнение методов

	Дообученная генеративно-состязательная сеть	Дообученный вариационный автокодировщик
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2014 (GAN); 2019–2020 (fine-tuning paradigm)	2014 (VAE); fine-tuning practice from 2015 onward
Автор метода	Goodfellow, I. et al. (GAN); fine-tuning practice established ~2019–2020	Kingma, D. P. & Welling, M. (VAE); fine-tuning strategy from transfer learning literature
Тип	Generative model (adversarial training + transfer)	Generative model with fine-tuning
Основополагающий источник	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.	Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).
Другие названия	Fine-Tuned GAN, GAN Fine-Tuning, Domain-Adapted GAN, Transfer GAN	fine-tuned VAE, domain-adapted VAE, transfer-learned VAE, adapted variational autoencoder
Связанные	6	6
Сводка	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.	A Fine-Tuned Variational Autoencoder begins with a VAE pre-trained on a large source dataset and then continues training on a smaller target-domain dataset. This approach adapts the learned latent representation and generative capacity to new data, preserving general structure while specializing to the target distribution — yielding better results than training from scratch when labeled or large target data is scarce.