  Disentangled encoding is an important step towards a better representation
learning. However, despite the numerous efforts, there still is no clear winner
that captures the independent features of the data in an unsupervised fashion.
In this work we empirically evaluate the performance of six unsupervised
disentanglement approaches on the mpi3d toy dataset curated and released for
the NeurIPS 2019 Disentanglement Challenge. The methods investigated in this
work are Beta-VAE, Factor-VAE, DIP-I-VAE, DIP-II-VAE, Info-VAE, and Beta-TCVAE.
The capacities of all models were progressively increased throughout the
training and the hyper-parameters were kept intact across experiments. The
methods were evaluated based on five disentanglement metrics, namely, DCI,
Factor-VAE, IRS, MIG, and SAP-Score. Within the limitations of this study, the
Beta-TCVAE approach was found to outperform its alternatives with respect to
the normalized sum of metrics. However, a qualitative study of the encoded
latents reveal that there is not a consistent correlation between the reported
metrics and the disentanglement potential of the model.
