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Stream/Area:
-
Subject:
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Paper ID:
IJSREM41480
Volume:
9 |
2 |
Feb 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are two of the most prominent generative models for image synthesis. This paper provides a comprehensive comparison of GANs and VAEs, focusing on their architectures, training methodologies, and performance in image generation tasks. We also evaluate their differences in terms of stability, quality, and diversity of outputs, supported by quantitative metrics such as Frechet Inception Distance (FID) and´ Inception Score (IS). A detailed analysis of their applications in various domains is presented, along with a discussion on their limitations and future directions.
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Publication Date
10/1/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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