Colorful Face Generation with VAEs

In this captivating project, we venture into the world of convolutional variational autoencoders (VAEs) to generate lifelike facial images. By utilizing the Labeled Faces in the Wild (LFW) dataset, which comprises a diverse collection of faces, we embark on an exciting journey to create a convolutional VAE capable of producing novel and realistic facial images.

The core of our project lies in the design of the convolutional VAE architecture. We create three distinct VAEs, each responsible for learning the latent representation of one RGB color channel (red, green, or blue). By combining the outputs of these VAEs, we synthesize vibrant and detailed full-color facial images. The training phase involves optimizing the VAEs using MSE loss and Kullback-Leibler (KL) divergence, which ensures accurate image reconstruction and disentangles latent representations. Let’s explore the art of face generation with convolutional VAEs and unlock the potential of generative deep learning models in creating lifelike visual content.