方法对比

	微调生成对抗网络	迁移学习GAN
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2014 (GAN); 2019–2020 (fine-tuning paradigm)	2014–2018
提出者	Goodfellow, I. et al. (GAN); fine-tuning practice established ~2019–2020	Goodfellow, I. et al. (GAN); Wang & Ramanan (transfer to GAN)
类型	Generative model (adversarial training + transfer)	Generative model with transferred weights
开创性文献	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.	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.
别名	Fine-Tuned GAN, GAN Fine-Tuning, Domain-Adapted GAN, Transfer GAN	TL-GAN, pretrained GAN, GAN fine-tuning, domain-adaptive GAN
相关	6	6
摘要	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.	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.