  Recent research has shown that generative models with highly disentangled
representations fail to generalise to unseen combination of generative factor
values. These findings contradict earlier research which showed improved
performance in out-of-training distribution settings when compared to entangled
representations. Additionally, it is not clear if the reported failures are due
to (a) encoders failing to map novel combinations to the proper regions of the
latent space or (b) novel combinations being mapped correctly but the
decoder/downstream process is unable to render the correct output for the
unseen combinations. We investigate these alternatives by testing several
models on a range of datasets and training settings. We find that (i) when
models fail, their encoders also fail to map unseen combinations to correct
regions of the latent space and (ii) when models succeed, it is either because
the test conditions do not exclude enough examples, or because excluded
generative factors determine independent parts of the output image. Based on
these results, we argue that to generalise properly, models not only need to
capture factors of variation, but also understand how to invert the generative
process that was used to generate the data.
