Σύγκριση μεθόδων

	Γενετικό Ανταγωνιστικό Δίκτυο	Wasserstein GAN (WGAN)
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2014	2017
Δημιουργός	Goodfellow, I. et al.	Martín Arjovsky, Soumith Chintala & Léon Bottou
Τύπος	Generative deep learning (adversarial two-network game)	Generative adversarial network variant
Θεμελιώδης πηγή	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein generative adversarial networks. International Conference on Machine Learning (ICML), 214–223.
Εναλλακτικές ονομασίες	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	WGAN, Earth-Mover GAN, Wasserstein Generative Adversarial Network, Wasserstein-GAN
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.	Wasserstein GAN (WGAN) is a generative adversarial network variant introduced by Arjovsky, Chintala, and Bottou in 2017 that replaces the Jensen-Shannon divergence used in the original GAN with the Wasserstein-1 (Earth Mover) distance. This substitution provides a theoretically grounded training objective that yields more stable optimization and a loss value that correlates meaningfully with generated sample quality, addressing the notorious mode collapse and vanishing gradient problems of standard GANs.