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We seek to learn a representation on a large annotated data source thatgeneralizes to a target domain using limited new supervision. Many priorapproaches to this problem have focused on learning "disentangled"representations so that as individual factors vary in a new domain, only aportion of the representation need be updated. In this work, we seek thegeneralization power of disentangled representations, but relax the requirementof explicit latent disentanglement and instead encourage linearity ofindividual factors of variation by requiring them to be manipulable by learnedlinear transformations. We dub these transformations latent canonicalizers, asthey aim to modify the value of a factor to a pre-determined (but arbitrary)canonical value (e.g., recoloring the image foreground to black). Assuming asource domain with access to meta-labels specifying the factors of variationwithin an image, we demonstrate experimentally that our method helps reduce thenumber of observations needed to generalize to a similar target domain whencompared to a number of supervised baselines.

Representation Learning Through Latent Canonicalizations