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Context: An oft-stated motivation for learning disentangled representations of data with deep generative models is a desire to achieve interpretability [5, 10]—particularly the decomposability [see §3.2.1 in 33] of latent representations to admit intuitive explanations.Most work has focused on capturing purely independent factors of variation [10, 7, 16, 25, 4, 57, 3, 8, 17, 15, 59], typically evaluating this using purpose-built, synthetic data [15, 17, 25], whose generative factors are independent by construction. In this work, we explored and analysed the fundamental characteristics of learning disentangled representations, and showed how these can be generalised to a more general framework of decomposition [33].We characterised the learning of decomposed latent representation with vaes in terms of the control of two factors:i) overlap in the latent space between encodings of different datapoints, andii) regularisation of the aggregate encoding distribution to the given prior, which encodes the structure one would wish for the latent space to have.

Question: What is the motivation behind learning disentangled representations of data ?
The motivation behind learning disentangled representations of data is a desire to achieve interpretability particularly the decomposability of latent representations to admit intuitive explanations. Another motivation of learning disentangled representations is to generalize framework of decomposition.