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• W4385584893 abstract "In recent years, deep learning and its subtopics have found a near gold-rush stature in the industry. This booming response has not been restricted to niche applications, but rather to titanic domains such as healthcare, self-driving cars, cybersecurity, and more. This “rise” has consequently led to a large influx of practitioners and users to this domain. One such subdomain is generative adversarial networks (GANs), an application of deep learning centered on image segmentation. The researchers aim to study the trajectory of and attempt to extrapolate the future of this subdomain in an attempt to discern if the meteoric rise of this technique is based on concrete positive results or a trend deemed to ebb. This study aims to first gather the most salient aspects and recent advancements of GANs. Specifically, the study emphasizes the importance of GANs and presents differing types utilized in various domains. Finally, the researchers present the current research gaps and the difficulties that could potentially be faced in the attainment of the aforementioned trajectory of this field." @default.
• W4385584893 created "2023-08-05" @default.
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• W4385584893 date "2023-06-30" @default.
• W4385584893 modified "2023-10-16" @default.
• W4385584893 title "Generative Adversarial Networks the Future of Consumer Deep Learning?" @default.
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• W4385584893 doi "https://doi.org/10.4018/978-1-6684-8306-0.ch012" @default.
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