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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Costa, C. (2025). Generative AI Models: A Comprehensive Review. OAE – Organizational Architect and Engineer Journal. Volume 7, Issue 3
Exportar Referência (IEEE) 
C. M. Costa,  "Generative AI Models: A Comprehensive Review", in OAE – Organizational Architect and Engineer Journal, vol. Volume 7, Issue 3, 2025
Exportar BibTeX 
@null{costa2025_1778745712885,
	year = "2025"
}
Exportar RIS 
TY  - GEN
TI  - Generative AI Models: A Comprehensive Review
T2  - OAE – Organizational Architect and Engineer Journal
VL  - Volume 7, Issue 3
AU  - Costa, C.
PY  - 2025
SN  - 2182-648X
DO  - 10.21428/b3658bca.d5d1872f
AB  - Generative Artificial Intelligence (AI) encompasses a diverse array of models designed to produce new data that closely resembles existing datasets, spanning modalities such as text, images, audio, and more. This review systematically categorizes and examines the primary generative model architectures: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, Transformer-based Models, Recurrent Neural Networks (RNNs), Energy-based Models (EBMs), and Reinforcement Learning (RL)-based generative approaches. For each model type, we discuss its foundational principles, representative architectures, and notable applications, providing insights into their respective strengths and limitations.
ER  -