  Irregular distribution in latent space causes posterior collapse,
misalignment between posterior and prior, and ill-sampling problem in
Variational Autoencoders (VAEs). In this paper, we introduce a novel adaptable
three-stage Uniform Transformation (UT) module -- Gaussian Kernel Density
Estimation (G-KDE) clustering, non-parametric Gaussian Mixture (GM) Modeling,
and Probability Integral Transform (PIT) -- to address irregular latent
distributions. By reconfiguring irregular distributions into a uniform
distribution in the latent space, our approach significantly enhances the
disentanglement and interpretability of latent representations, overcoming the
limitation of traditional VAE models in capturing complex data structures.
Empirical evaluations demonstrated the efficacy of our proposed UT module in
improving disentanglement metrics across benchmark datasets -- dSprites and
MNIST. Our findings suggest a promising direction for advancing representation
learning techniques, with implication for future research in extending this
framework to more sophisticated datasets and downstream tasks.
