  When designing variational autoencoders (VAEs) or other types of latent space
models, the dimensionality of the latent space is typically defined upfront. In
this process, it is possible that the number of dimensions is under- or
overprovisioned for the application at hand. In case the dimensionality is not
predefined, this parameter is usually determined using time- and
resource-consuming cross-validation. For these reasons we have developed a
technique to shrink the latent space dimensionality of VAEs automatically and
on-the-fly during training using Generalized ELBO with Constrained Optimization
(GECO) and the $L_0$-Augment-REINFORCE-Merge ($L_0$-ARM) gradient estimator.
The GECO optimizer ensures that we are not violating a predefined upper bound
on the reconstruction error. This paper presents the algorithmic details of our
method along with experimental results on five different datasets. We find that
our training procedure is stable and that the latent space can be pruned
effectively without violating the GECO constraints.
