Taskonomy: Disentangling Task Transfer Learning

Taskonomy: Disentangling Task Transfer LearningAmir R. Zamir1,2 Alexander Sax1 William Shen1 Leonidas Guibas1 Jitendra Malik2 Silvio Savarese11 Stanford University2 University of California, visual tasks have a relationship, or are they unre-lated? For instance, could having surface normals sim-plify estimating the depth of an image? Intuition answersthese questions positively, implying existence of astructureamong visual tasks. Knowing this structure has notable val-ues; it is the concept underlying Transfer Learning and pro-vides a principled way for identifying redundancies acrosstasks, in order to, for instance, seamlessly reuse supervi-sion among related tasks or solve many tasks in one systemwithout piling up the propose a fully computational approach for model-ing the structure of the space of visual tasks.

ing provably efficient comprehensive/universal perception models [34,4], i.e. ones that can solve a large set of tasks before becoming intractable in supervision or computation

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