November 22, 2025Discover NetworksOpen Access

Advancements and challenges in the development of generative adversarial network (GANs) for deep learning

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Authors

KIKashif Iqbal ARAtifa Rafique SQSara Qaisar

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Implication

This survey observes advancements in generative adversarial networks, outlining challenges in synthetic data creation and applications in various fields.

Key Points

Generative adversarial networks advance deep learning, creating high-quality synthetic data amidst persistent challenges.
This survey provides a comprehensive taxonomy and evaluations on benchmarks like CIFAR-10 and STL-10.
Developed frameworks address instability, mode collapse, and optimization issues faced by GANs.
Future directions involve enhancing GANs for diverse applications such as computer vision and medical imaging.

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Iqbal et al. (2025) studied this question.

synapsesocial.com/papers/6924e3ddc0ce034ddc34e8ef https://doi.org/10.1007/s44354-025-00007-w

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Implication

This survey observes advancements in generative adversarial networks, outlining challenges in synthetic data creation and applications in various fields.

Advancements and challenges in the development of generative adversarial network (GANs) for deep learning

Discussion

Member takes

Implication

Key Points

Cite This Study

Advancements and challenges in the development of generative adversarial network (GANs) for deep learning

Discussion

Member takes

Implication

Key Points

Cite This Study

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