Shape your Space: AGaussian Mixture Regularization Approach to Deterministic Autoencoders

In this document, we provide additional details and results to the main paper. The document is structured as follows: A.1 Loss Analysis - Analysis of the unimodal and multimodal latent regularization loss across different distributions and an ablation study on the proposed loss function. A.2 Image Generation - In this section, we compare VQVAE model with our method, provide detailed descriptions of the dataset, network architecture, and implementation details of the image generation experiments in the main paper. A.3 Modelling Discrete Structures - In this section, we describe the experimental and implementation details of the discrete data structure experiments in the main paper. A.5 Additional Qualitative Analysis - More examples of the randomly generated samples of MNIST, FASHIONMNIST, SVHN and CELEBA images.