  Revealing latent structure in data is an active field of research, having
introduced exciting technologies such as variational autoencoders and
adversarial networks, and is essential to push machine learning towards
unsupervised knowledge discovery. However, a major challenge is the lack of
suitable benchmarks for an objective and quantitative evaluation of learned
representations. To address this issue we introduce Morpho-MNIST, a framework
that aims to answer: "to what extent has my model learned to represent specific
factors of variation in the data?" We extend the popular MNIST dataset by
adding a morphometric analysis enabling quantitative comparison of trained
models, identification of the roles of latent variables, and characterisation
of sample diversity. We further propose a set of quantifiable perturbations to
assess the performance of unsupervised and supervised methods on challenging
tasks such as outlier detection and domain adaptation. Data and code are
available at https://github.com/dccastro/Morpho-MNIST.
