  $\beta$-VAE is a follow-up technique to variational autoencoders that
proposes special weighting of the KL divergence term in the VAE loss to obtain
disentangled representations. Unsupervised learning is known to be brittle even
on toy datasets and a meaningful, mathematically precise definition of
disentanglement remains difficult to find. Here we investigate the original
$\beta$-VAE paper and add evidence to the results previously obtained
indicating its lack of reproducibility. We also further expand the
experimentation of the models and include further more complex datasets in the
analysis. We also implement an FID scoring metric for the $\beta$-VAE model and
conclude a qualitative analysis of the results obtained. We end with a brief
discussion on possible future investigations that can be conducted to add more
robustness to the claims.

Machine Learning;Artificial Intelligence