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Disentangled encoding is an important step towards a better representationlearning. However, despite the numerous efforts, there still is no clear winnerthat captures the independent features of the data in an unsupervised fashion.In this work we empirically evaluate the performance of six unsuperviseddisentanglement approaches on the mpi3d toy dataset curated and released forthe NeurIPS 2019 Disentanglement Challenge. The methods investigated in thiswork 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 thetraining and the hyper-parameters were kept intact across experiments. Themethods were evaluated based on five disentanglement metrics, namely, DCI,Factor-VAE, IRS, MIG, and SAP-Score. Within the limitations of this study, theBeta-TCVAE approach was found to outperform its alternatives with respect tothe normalized sum of metrics. However, a qualitative study of the encodedlatents reveal that there is not a consistent correlation between the reportedmetrics and the disentanglement potential of the model.

A Preliminary Study of Disentanglement With Insights on the Inadequacy of Metrics