Deep Convolutional GANs (DCGANs) are a type of generative model that use deep learning techniques to create realistic images. They consist of two neural networks: a generator that creates images and a discriminator that evaluates them. The generator learns to produce images that resemble real data, while the discriminator learns to distinguish between real and generated images. DCGANs utilize convolutional layers, which are effective for processing image data. This architecture allows the model to capture spatial hierarchies in images, leading to improved quality in the generated outputs. They are widely used in applications like image synthesis and style transfer, contributing to advancements in artificial intelligence.