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This is a Literature Review study.

September 10, 2025

Generative Adversarial Networks for Synthetic Data Generation in Deep Learning Applications

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GANs generate high-fidelity synthetic data, improving model robustness and generalization across various domains.
Analysis reveals that GANs outperform other methods, such as Variational Autoencoders, in generating complex data types.
Exploring techniques like DCGANs and CycleGANs, the review shows the versatility of GANs in applications from healthcare to finance.
Ethical considerations are critical as GANs pose risks of misuse, necessitating robust evaluation benchmarks for safer deployment.

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Implication

This synthesis review demonstrates GANs improve data quality in deep learning, highlighting their role in privacy preservation and addressing algorithmic bias.

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Mohamed Islam Keskes (2025) studied this question.

synapsesocial.com/papers/68c1a25a54b1d3bfb60dd2fa https://doi.org/10.29328/journal.jairi.1001004

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Implication

This synthesis review demonstrates GANs improve data quality in deep learning, highlighting their role in privacy preservation and addressing algorithmic bias.

Generative Adversarial Networks for Synthetic Data Generation in Deep Learning Applications

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Discussion

Implication

Cite This Study

Generative Adversarial Networks for Synthetic Data Generation in Deep Learning Applications

Key Points

Discussion

Implication

Cite This Study

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