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A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. [ 1 ]
What came out of that fateful meeting was “generative adversarial network” or (GAN), an innovation that AI experts have described as the “coolest idea in deep learning in the last 20 years.”
Above: An image classifier, an example of a neural network trained with a discriminative objective. Below: A text-to-image model, an example of a network trained with a generative objective. Since its inception, the field of machine learning used both discriminative models and generative models, to model and predict data.
A computer network is a set of computers sharing resources located on or provided ... (GAN) is a network used for supporting mobile users across an arbitrary number ...
The Wasserstein Generative Adversarial Network (WGAN) is a variant of generative adversarial network (GAN) proposed in 2017 that aims to "improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches".
Network-attached storage (NAS) is a file-level computer data storage server connected to a computer network providing data access to a heterogeneous group of clients. In this context, the term "NAS" can refer to both the technology and systems involved, or a specialized computer appliance device unit built for such functionality – a NAS ...
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In addition, GAN relieves congestion (meaning that networks can, through GAN, essentially piggyback on other infrastructure) on the GSM or UMTS spectrum by removing common types of calls and routing them to the operator via the relatively low-cost Internet. [14] GAN makes sense for network operators that also offer Internet services.