Salīdzināt metodes

	Pārneses apmācība ar variācijas autoenkoderi	Pielāgotā ģeneratīvā pretestības tīkls
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2014 (VAE); 2010 (transfer learning survey)	2014 (GAN); 2019–2020 (fine-tuning paradigm)
Autors	Kingma, D. P. & Welling, M. (VAE); transfer learning framework from Pan & Yang	Goodfellow, I. et al. (GAN); fine-tuning practice established ~2019–2020
Tips	Generative model with transferred encoder/decoder	Generative model (adversarial training + transfer)
Pirmavots	Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR 2014).	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	TL-VAE, pretrained VAE, VAE transfer learning, fine-tuned variational autoencoder	Fine-Tuned GAN, GAN Fine-Tuning, Domain-Adapted GAN, Transfer GAN
Saistītās	6	6
Kopsavilkums	Transfer Learning with a Variational Autoencoder (TL-VAE) reuses an encoder and/or decoder pre-trained on a large source dataset and adapts it to a smaller target domain. By inheriting a rich probabilistic latent space rather than starting from random weights, TL-VAE dramatically reduces the amount of target-domain data needed for high-quality generation or representation learning.	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.