Σύγκριση μεθόδων

	Γενετικό Ανταγωνιστικό Δίκτυο	Εκμάθηση μεταφοράς
Πεδίο	Βαθιά Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2014	2010 (formalized); 1990s (early roots)
Δημιουργός	Goodfellow, I. et al.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Τύπος	Generative deep learning (adversarial two-network game)	Learning paradigm
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Συναφείς	4	3
Σύνοψη	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.