Deep Convolutional GANs (DCGANs) represent a pivotal advancement in the evolution of Generative Adversarial Networks, specifically designed to harness the power of convolutional neural networks (CNNs) for image generation tasks. Unlike the original GAN architecture, which typically relies on fully connected layers, DCGANs employ convolutional layers in both the generator and discriminator. This architectural shift enables DCGANs to better capture spatial hierarchies in images, resulting in more realistic and coherent generated outputs.

Key architectural features of DCGANs include:

• Use of batch normalization in both the generator and discriminator to stabilize training and accelerate convergence;
• Replacement of pooling layers with strided convolutions in the discriminator and fractional-strided (transposed) convolutions in the generator, allowing the networks to learn their own spatial downsampling and upsampling;
• Removal of fully connected hidden layers for deeper architectures, relying instead on convolutional layers for feature extraction and generation;
• Adoption of ReLU activations in the generator (except for the output layer, which uses Tanh) and LeakyReLU activations in the discriminator.

Here are concise pseudocode outlines of DCGAN generator and discriminator architectures, emphasizing their main layers and data flow:

DCGAN Generator:

• Input: random noise vector (z);
• Dense layer, reshape to image-like tensor;
• Stacked transposed convolutions, each with batch normalization and ReLU;
• Output: transposed convolution with Tanh activation (generates image).

DCGAN Discriminator:

• Input: image;
• Stacked strided convolutions, each with batch normalization and LeakyReLU;
• Output: flatten, dense layer with Sigmoid activation (real/fake probability).

DCGANs use these convolutional designs to generate more realistic images than fully connected GANs.