A tutorial on multi-view autoencoders using the multi-view-AE library
Ana Lawry Aguila Andre Altmann
There has been a growing interest in recent years in modelling multiple
modalities (or views) of data to for example, understand the relationship
between modalities or to generate missing data. Multi-view autoencoders have
gained significant traction for their adaptability and versatility in modelling
multi-modal data, demonstrating an ability to tailor their approach to suit the
characteristics of the data at hand. However, most multi-view autoencoders have
inconsistent notation and are often implemented using different coding
frameworks. To address this, we present a unified mathematical framework for
multi-view autoencoders, consolidating their formulations. Moreover, we offer
insights into the motivation and theoretical advantages of each model. To
facilitate accessibility and practical use, we extend the documentation and
functionality of the previously introduced \texttt{multi-view-AE} library. This
library offers Python implementations of numerous multi-view autoencoder
models, presented within a user-friendly framework. Through benchmarking
experiments, we evaluate our implementations against previous ones,
demonstrating comparable or superior performance. This work aims to establish a
cohesive foundation for multi-modal modelling, serving as a valuable
educational resource in the field.