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MNIST-DCGAN is a deep learning project that uses a DCGAN to generate realistic handwritten digits from the MNIST dataset. It demonstrates how a generator and discriminator network compete to create and evaluate images, improving the generator’s output over time.
A Deep Convolutional Generative Adversarial Network (DCGAN) is an extension of the standard GAN architecture that uses deep convolutional networks for both the generator and discriminator models.
Implementation of DCGAN model to train a neural network on mnist dataset and generate fake handwritten digits close enough to the real images from the dataset.
This 'Generative Adversarial Network' project was implemented in grad course CSE-676 : Deep Learning [Fall 2019 @UB_SUNY] Course Instructor : Sargur N. Srihari(https://cedar.buffalo.edu/~srihari/)