Generating Human Face with Dcgan and Gan

Generative Adversarial Networks (GANs) are prominent in unsupervised learning for their exceptional data-generation capabilities. GANs utilize backpropagation and a competitive process between a Generative Network (G) and a Discriminative Network (D). In this setup, G generates artificial images while D distinguishes real from artificial ones, enhancing G's ability to create realistic images. Deep Convolutional Generative Adversarial Networks (DCGAN) are particularly notable, using a convolutional architecture to produce high-quality human face images. This study trains DCGAN on the CelebFaces Attributes Dataset (CelebA), demonstrating its ability to generate human faces from unlabeled data and random noise. Evaluation is done quantitatively using the Structural Similarities Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) to assess image quality. Additionally, this abstract will compare the effectiveness of GANs and DCGANs in human face generation.