Recent advancements in deep generative modeling replace the loss function with features extracted from pre-trained models for better generative capabilities. This is true for the various types of generative models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). In this project, we will investigate the need for these pre-trained networks and try to replace them with a bootstrapped version of our generative model. The project may involve different data types, such as images, 3D data or tabular data. The students will build their own generative model and try to improve the generative capabilities over a baseline algorithm, gaining hands-on experience in deep learning, unsupervised learning and deep generative models.