How to Design and Train Generative Adversarial Networks (GANs)
Get an overview of generative adversarial networks (GANs) and walk through how to design and train one using MATLAB®.
GANs are composed of two deep neural networks, a generator and a discriminator, which are adversaries of each other (thus the term “adversarial”). The generator creates new data instances, while the discriminator evaluates them for authenticity (i.e., it decides whether each instance of data that it reviews belongs to the actual training dataset or not).
Additional Resources:
- Example of how to train a generative adversarial network to generate images.: https://bit.ly/3f1cTpd
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Get an overview of generative adversarial networks (GANs) and walk through how to design and train one using MATLAB®.
GANs are composed of two deep neural networks, a generator and a discriminator, which are adversaries of each other (thus the term “adversarial”). The generator creates new data instances, while the discriminator evaluates them for authenticity (i.e., it decides whether each instance of data that it reviews belongs to the actual training dataset or not).
Additional Resources:
– Example of how to train a generative adversarial network to generate images.: https://bit.ly/3f1cTpd
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Get a free product trial: https://goo.gl/ZHFb5u
Learn more about MATLAB: https://goo.gl/8QV7ZZ
Learn more about Simulink: https://goo.gl/nqnbLe
See what’s new in MATLAB and Simulink: https://goo.gl/pgGtod
© 2020 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
