Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders

Understand

Discrete latent spaces in variational autoencoders have been shown to effectively capture the data distribution for many real-world problems such as natural language understanding, human intent prediction, and visual scene representation.

• However, discrete latent spaces need to be sufficiently large to capture the complexities of real-world data, rendering downstream tasks computationally challenging.
• For instance, performing motion planning in a high-dimensional latent representation of the environment could be intractable.
• We consider the problem of sparsifying the discrete latent space of a trained conditional variational autoencoder, while preserving its learned multimodality.