Generative Adversarial Network (GANs) Deep Learning – Day 76

  Exploring the Evolution of GANs: From DCGANs to StyleGANs Generative Adversarial Networks (GANs) have revolutionized the field of image generation by allowing us to create realistic images from random noise. Over the years, the basic architecture of GANs has undergone significant enhancements, resulting in more stable and higher-quality image generation. In this post, we will dive deep into three key stages of GAN development: Deep Convolutional GANs (DCGANs), Progressive Growing of GANs, and StyleGANs. Deep Convolutional GANs (DCGANs) The introduction of Deep Convolutional GANs (DCGANs) in 2015 by Alec Radford and colleagues marked a major breakthrough in stabilizing GAN training and improving image generation. DCGANs leveraged deep convolutional layers to enhance image quality, particularly for larger images. Key Guidelines for DCGANs Guideline Description Strided Convolutions Replace pooling layers with strided convolutions in the discriminator and transposed convolutions in the generator. Batch Normalization Use batch normalization in all layers except the generator’s output layer and the discriminator’s input layer. No Fully Connected Layers Remove fully connected layers to enhance training stability and performance. Activation Functions Use ReLU in the generator (except for the output layer, which uses tanh) and Leaky ReLU in the discriminator. DCGAN Architecture Example In the table…