Salīdzināt metodes

	Generatīvais Adversariālais Tīkls	Detekcija ārpus sadalījuma
Nozare	Dziļā mācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2014	2017
Autors	Goodfellow, I. et al.	Hendrycks & Gimpel
Tips	Generative deep learning (adversarial two-network game)	Reliability and safety method for neural networks
Pirmavots	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Hendrycks, D., & Gimpel, K. (2017). A baseline for detecting misclassified and out-of-distribution examples in neural networks. International Conference on Learning Representations.
Citi nosaukumi	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	OOD Detection, Novelty Detection, Open-Set Recognition, Dağılım Dışı Tespit
Saistītās	4	3
Kopsavilkums	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.	Out-of-Distribution (OOD) detection is a set of techniques that identify when a deployed machine learning model receives inputs that differ significantly from its training data distribution. Introduced as a formal problem by Hendrycks and Gimpel in 2017, these methods enable models to flag unfamiliar inputs rather than silently produce unreliable predictions, making them foundational to trustworthy and safe AI deployment in high-stakes domains.