Multiple Random Masking Autoencoder Ensembles for Robust Multimodal
  Semi-supervised Learning
Alexandru-Raul Todoran Marius Leordeanu
There is an increasing number of real-world problems in computer vision and
machine learning requiring to take into consideration multiple interpretation
layers (modalities or views) of the world and learn how they relate to each
other. For example, in the case of Earth Observations from satellite data, it
is important to be able to predict one observation layer (e.g. vegetation
index) from other layers (e.g. water vapor, snow cover, temperature etc), in
order to best understand how the Earth System functions and also be able to
reliably predict information for one layer when the data is missing (e.g. due
to measurement failure or error).