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Generative Adversarial Networks, or GANs, are an approach to generative modeling using deep learning methods, such as convolutional neural networks.
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It is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in 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.
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The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits that is commonly used for training various image processing systems.
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The database is also widely used for training and testing in the field of machine learning.
