DCGAN

A DCGAN (Deep Convolutional Generative Adversarial Network) is a type of neural network that creates realistic images by training two components: a generator and a discriminator. The generator tries to produce images that look real, starting from random noise, while the discriminator evaluates whether images are real or fake. Through iterative training, the generator improves its images to fool the discriminator, leading to increasingly convincing synthetic images. DCGANs use convolutional layers, which are effective at capturing spatial patterns, making them particularly good at generating high-quality visuals like faces or objects.