Generalized Multimodal ELBO

Thomas M. Sutter, Imant Daunhawer, Julia E Vogt

Keywords: Multimodal, VAE, ELBO, self-supervised, generative learning

Abstract: 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.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

One-sentence Summary: We propose a generalized ELBO for modeling multiple data types in a scalable and self-supervised way.

Code: [ thomassutter/MoPoE](https://github.com/thomassutter/MoPoE)

Data: [PolyMNIST](https://paperswithcode.com/dataset/polymnist), [CelebA](https://paperswithcode.com/dataset/celeba), [SVHN](https://paperswithcode.com/dataset/svhn)

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2105.02470/code)