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A Deep Analysis on the Role of Deep Learning Models Using Generative Adversarial Networks

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Blockchain and Deep Learning

Part of the book series: Studies in Big Data ((SBD,volume 105))

Abstract

A comparatively novel advance field of deep learning is the Generative Adversarial Network called GAN. These different types of networks if they start working in line with each other and work not to get the better of each other but start working keeping arm to arm connected to each other's world will be different. GAN is a category of machine learning frameworks intended for generation of images in which neural networks challenge each other in format of playing game. One network generates metaphors also called the generator and an additional network attempt to differentiate between the fake and real from the data set called as the discriminator. If suppose a training set is presented this procedure guides to manufacture a innovative information comparable to training set. Pictures created from GAN are same metaphors giving the notion of genuine to any observer having real features. GAN networks work on supervised, unsupervised and for reinforcement learning but earlier it applies or used only unsupervised learning only. This generative network produces candidate and on the other hand, the discriminative network evaluate them. Here producer is a complex neural network and differentiator is a convolution neural network. The GAN network divided into three categories to produce generative model learns and generation of data by probabilistic ideas. Next training of model can be completed in contradictory state. Lastly for training using neural networks, deep learning, and artificial intelligence methods. If generative networks used deep leaning methods then deep learning models can employ a very large amount of dataset, heavily dependent on high-end Machines, tries to solves problem from end to end machines, takes longer time to train means the results are better after getting trained on the other hand takes lesser time to test the data. Applications of GAN networks are increasing day by day as it is touching every sphere of our day today life. Some of the benefits of the deep learning are to creating artificial Intelligence function which mimics the mechanism of human brain in handing out data for decision making. Deep learning if combined with artificial intelligence can be capable of learning data which is considered unlabeled and unstructured. The chapter will relate different models of deep learning and their efficiency can be measured by studying different methods, models and simulation techniques.

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Author information

Authors and Affiliations

  1. Panipat Institute of Engineering and Technology, Samalkha, Haryana, 132101, India

    Alankrita Aggarwal, Shivani Gaba, Shally Nagpal & Anoopa Arya

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  1. Alankrita Aggarwal
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  2. Shivani Gaba
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  3. Shally Nagpal
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  4. Anoopa Arya
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Editor information

Editors and Affiliations

  1. School of Computing, Southern Illinois University, Carbondale, IL, USA

    Khaled R. Ahmed

  2. Computer Science, Southern Illinois University, Carbondale, IL, USA

    Henry Hexmoor

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Aggarwal, A., Gaba, S., Nagpal, S., Arya, A. (2022). A Deep Analysis on the Role of Deep Learning Models Using Generative Adversarial Networks. In: Ahmed, K.R., Hexmoor, H. (eds) Blockchain and Deep Learning. Studies in Big Data, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-030-95419-2_9

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