手法を比較

	ファインチューニングされた敵対的生成ネットワーク	ファインチューニングされた拡散モデル
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2014 (GAN); 2019–2020 (fine-tuning paradigm)	2020–2023
提唱者	Goodfellow, I. et al. (GAN); fine-tuning practice established ~2019–2020	Ho, J., Jain, A., Abbeel, P. (base DDPM); Ruiz et al. (DreamBooth fine-tuning paradigm)
種類	Generative model (adversarial training + transfer)	Generative model (fine-tuned via subject-specific or domain-specific data)
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.	Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., & Aberman, K. (2023). DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 22500–22510.
別名	Fine-Tuned GAN, GAN Fine-Tuning, Domain-Adapted GAN, Transfer GAN	DDPM fine-tuning, diffusion model adaptation, personalized diffusion model, subject-driven diffusion fine-tuning
関連	6	5
概要	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 diffusion model adapts a large pretrained denoising diffusion model — such as Stable Diffusion or DALL-E — to a specific subject, style, or domain by continuing training on a small curated dataset. Techniques such as DreamBooth, textual inversion, and LoRA make this adaptation feasible on consumer hardware while preserving general generative capability.