Project Title: Comparative Analysis of GAN Networks

• Description: Implemented and compared three Generative Adversarial Networks (GANs): ACGAN (Auxiliary Classifier GAN), DCGAN (Deep Convolutional GAN), and WGAN (Wasserstein GAN). • Key Points: • Implemented ACGAN, DCGAN, and WGAN architectures. • Compared performance metrics such as convergence speed, image quality, and stability. • Evaluated each GAN’s suitability for different generative tasks.