  Do visual tasks have a relationship, or are they unrelated? For instance,
could having surface normals simplify estimating the depth of an image?
Intuition answers these questions positively, implying existence of a structure
among visual tasks. Knowing this structure has notable values; it is the
concept underlying transfer learning and provides a principled way for
identifying redundancies across tasks, e.g., to seamlessly reuse supervision
among related tasks or solve many tasks in one system without piling up the
complexity.
  We proposes a fully computational approach for modeling the structure of
space of visual tasks. This is done via finding (first and higher-order)
transfer learning dependencies across a dictionary of twenty six 2D, 2.5D, 3D,
and semantic tasks in a latent space. The product is a computational taxonomic
map for task transfer learning. We study the consequences of this structure,
e.g. nontrivial emerged relationships, and exploit them to reduce the demand
for labeled data. For example, we show that the total number of labeled
datapoints needed for solving a set of 10 tasks can be reduced by roughly 2/3
(compared to training independently) while keeping the performance nearly the
same. We provide a set of tools for computing and probing this taxonomical
structure including a solver that users can employ to devise efficient
supervision policies for their use cases.
