  Multiple data types naturally co-occur when describing real-world phenomena
and learning from them is a long-standing goal in machine learning research.
However, existing self-supervised generative models approximating an ELBO are
not able to fulfill all desired requirements of multimodal models: their
posterior approximation functions lead to a trade-off between the semantic
coherence and the ability to learn the joint data distribution. We propose a
new, generalized ELBO formulation for multimodal data that overcomes these
limitations. The new objective encompasses two previous methods as special
cases and combines their benefits without compromises. In extensive
experiments, we demonstrate the advantage of the proposed method compared to
state-of-the-art models in self-supervised, generative learning tasks.
