So sánh phương pháp

	Transfer Learning GAN	Generative Adversarial Network
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2014–2018	2014
Người khởi xướng	Goodfellow, I. et al. (GAN); Wang & Ramanan (transfer to GAN)	Goodfellow, I. et al.
Loại	Generative model with transferred weights	Generative deep learning (adversarial two-network game)
Công trình gốc	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.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
Tên gọi khác	TL-GAN, pretrained GAN, GAN fine-tuning, domain-adaptive GAN	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Liên quan	6	4
Tóm tắt	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.	A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation.