  Embedded spaces are a key feature in deep learning. Good embedded spaces
represent the data well to support classification and advanced techniques such
as open-set recognition, few-short learning and explainability. This paper
presents a compact overview of different techniques to design embedded spaces
for classification. It compares different loss functions and constraints on the
network parameters with respect to the achievable geometric structure of the
embedded space. The techniques are demonstrated with two and three-dimensional
embeddings for the MNIST, Fashion MNIST and CIFAR-10 datasets, allowing visual
inspection of the embedded spaces.
