  Variational autoencoders (VAEs) face a notorious problem wherein the
variational posterior often aligns closely with the prior, a phenomenon known
as posterior collapse, which hinders the quality of representation learning. To
mitigate this problem, an adjustable hyperparameter $\beta$ and a strategy for
annealing this parameter, called KL annealing, are proposed. This study
presents a theoretical analysis of the learning dynamics in a minimal VAE. It
is rigorously proved that the dynamics converge to a deterministic process
within the limit of large input dimensions, thereby enabling a detailed
dynamical analysis of the generalization error. Furthermore, the analysis shows
that the VAE initially learns entangled representations and gradually acquires
disentangled representations. A fixed-point analysis of the deterministic
process reveals that when $\beta$ exceeds a certain threshold, posterior
collapse becomes inevitable regardless of the learning period. Additionally,
the superfluous latent variables for the data-generative factors lead to
overfitting of the background noise; this adversely affects both generalization
and learning convergence. The analysis further unveiled that appropriately
tuned KL annealing can accelerate convergence.
