Σύγκριση μεθόδων

	Γενετικό Ανταγωνιστικό Δίκτυο	Ανάλυση Κύριων Συνιστωσών
Πεδίο	Βαθιά Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2014	2002
Δημιουργός	Goodfellow, I. et al.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Τύπος	Generative deep learning (adversarial two-network game)	Unsupervised dimensionality reduction
Θεμελιώδης πηγή	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Εναλλακτικές ονομασίες	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
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.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.