{"id":827954,"date":"2021-04-24T03:33:38","date_gmt":"2021-04-24T07:33:38","guid":{"rendered":"https:\/\/www.analyticsvidhya.com\/?p=78130"},"modified":"2021-04-24T03:33:38","modified_gmt":"2021-04-24T07:33:38","slug":"generate-your-own-dataset-using-gan","status":"publish","type":"station","link":"https:\/\/platodata.io\/plato-data\/generate-your-own-dataset-using-gan\/","title":{"rendered":"Generate Your Own Dataset using GAN"},"content":{"rendered":"
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G<\/span>enerative adversarial networks (GANs), is an algorithmic architecture that consists of two neural networks, which are in competition with each other (thus the \u201cadversarial\u201d) in order to generate new, replicated instances of data that can pass for real data. <\/span><\/p>\n The generative approach is an unsupervised learning method in machine learning which involves automatically discovering and learning the patterns or regularities in the given input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset<\/span> Their applications include image generation, video generation, and voice generation.<\/p>\n