手法を比較

	転移学習GAN	Generative Adversarial Network
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2014–2018	2014
提唱者	Goodfellow, I. et al. (GAN); Wang & Ramanan (transfer to GAN)	Goodfellow, I. et al.
種類	Generative model with transferred weights	Generative deep learning (adversarial two-network game)
原典	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.
別名	TL-GAN, pretrained GAN, GAN fine-tuning, domain-adaptive GAN	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
関連	6	4
概要	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.