Abstract
This project uses Deep Convolutional Generative Adversarial Networks (DCGAN) to generate realistic images from random noise. The model consists of a generator that creates images and a discriminator that evaluates them, improving image quality through adversarial training. DCGANs are widely used in image synthesis, data augmentation, and creative AI applications.
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