DCGAN
DCGAN, or Deep Convolutional Generative Adversarial Network, is a type of generative adversarial network (GAN) architecture designed to generate high-quality images using deep convolutional neural networks. Introduced in 2015 by Ian Goodfellow and his colleagues, DCGAN builds upon the foundational GAN framework by incorporating convolutional layers, which are particularly effective for spatial data like images.
A GAN consists of two neural networks: a generator and a discriminator. The generator creates synthetic data,
Key design principles of DCGAN include using strided convolutions instead of pooling layers for downsampling, batch
DCGAN has been widely adopted for tasks such as image synthesis, super-resolution, and data augmentation. Its