The manifold hypothesis states that high-dimensional real-world data typically lies in a lower-dimensional submanifold, the axes of this dimensionality-reduced space representing factors of variation [26, 5]. Relatedly, the flattening hypothesis [2] and work in disentanglement [22] states that througout learning, subsequent layers in a deep neural network (DNN) disentangle the data in such a way that finally a linear model can separate the classes. Understanding how a DNN itself views its input space can be related to explainability (e.g.

A new method for multimodal sensor fusion is introduced. The technique relies on a two-stage process. In the first stage, a multimodal generative model is constructed from unlabelled training data. In the second stage, the generative model serves as a reconstruction prior and the search manifold for the sensor fusion tasks. The method also handles cases where observations are accessed only via subsampling i.e. compressed sensing. We demonstrate the effectiveness and excellent performance on a range of multimodal fusion experiments such as multisensory classification, denoising, and recovery from subsampled observations.