So sánh phương pháp

	Generative Adversarial Network	Transfer Learning	Bộ tự mã hóa biến phân
Lĩnh vực	Học sâu	Học máy	Học sâu
Họ	Machine learning	Machine learning	Machine learning
Năm ra đời	2014	2010 (formalized); 1990s (early roots)	2014
Người khởi xướng	Goodfellow, I. et al.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)	Kingma, D. P. & Welling, M.
Loại	Generative deep learning (adversarial two-network game)	Learning paradigm	Deep generative latent-variable model (encoder–decoder)
Công trình gốc	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Tên gọi khác	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	TL, domain adaptation, fine-tuning, pre-trained model adaptation	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Liên quan	4	3	5
Tóm tắt	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.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.	The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning.