The field of deep generative models and autoencoders is rapidly evolving, with a focus on improving the tractability and expressiveness of these models. Recent developments have centered around distilling complex models into more tractable forms, while preserving their generative capabilities. This has led to the creation of more efficient and effective models for tasks such as density estimation, conditional generation, and model order reduction. Additionally, there is a growing interest in using autoencoders for non-intrusive model order reduction in continuum mechanics, as well as for predicting outcomes from complex signals. Noteworthy papers in this area include:

• A paper that demonstrates the distillation of a VQ-VAE into a tractable model, preserving its expressiveness while providing tractable probabilistic inference.
• A paper that proposes a Conditional-$t^3$VAE, which achieves equitable latent space allocation across classes, leading to improved generative fairness and diversity, particularly under severe class imbalance.