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Unsupervised disentanglement has been shown to be theoretically impossiblewithout inductive biases on the models and the data. As an alternativeapproach, recent methods rely on limited supervision to disentangle the factorsof variation and allow their identifiability. While annotating the truegenerative factors is only required for a limited number of observations, weargue that it is infeasible to enumerate all the factors of variation thatdescribe a real-world image distribution. To this end, we propose a method fordisentangling a set of factors which are only partially labeled, as well asseparating the complementary set of residual factors that are never explicitlyspecified. Our success in this challenging setting, demonstrated on syntheticbenchmarks, gives rise to leveraging off-the-shelf image descriptors topartially annotate a subset of attributes in real image domains (e.g. of humanfaces) with minimal manual effort. Specifically, we use a recent language-imageembedding model (CLIP) to annotate a set of attributes of interest in azero-shot manner and demonstrate state-of-the-art disentangled imagemanipulation results.

An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild