A tutorial on multi-view autoencoders using the multi-view-AE library
<|reference_start|>A tutorial on multi-view autoencoders using the multi-view-AE library: 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.<|reference_end|>
@article{aguila2024a,
    title={A tutorial on multi-view autoencoders using the multi-view-AE library},
    author={Ana Lawry Aguila, Andre Altmann},
    journal={arXiv preprint arXiv:2403.07456},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07456},
    primaryClass={cs.LG stat.ML}
}