विधियों की तुलना करें

	जनरेटिव एडवरसैरियल नेटवर्क	प्रिंसिपल कंपोनेंट एनालिसिस
क्षेत्र	गहन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2014	2002
प्रवर्तक	Goodfellow, I. et al.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
प्रकार	Generative deep learning (adversarial two-network game)	Unsupervised dimensionality reduction
मौलिक स्रोत	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
उपनाम	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
संबंधित	4	3
सारांश	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.