  Exploring the transferability between heterogeneous tasks sheds light on
their intrinsic interconnections, and consequently enables knowledge transfer
from one task to another so as to reduce the training effort of the latter. In
this paper, we propose an embarrassingly simple yet very efficacious approach
to estimating the transferability of deep networks, especially those handling
vision tasks. Unlike the seminal work of taskonomy that relies on a large
number of annotations as supervision and is thus computationally cumbersome,
the proposed approach requires no human annotations and imposes no constraints
on the architectures of the networks. This is achieved, specifically, via
projecting deep networks into a model space, wherein each network is treated as
a point and the distances between two points are measured by deviations of
their produced attribution maps. The proposed approach is several-magnitude
times faster than taskonomy, and meanwhile preserves a task-wise topological
structure highly similar to the one obtained by taskonomy. Code is available at
https://github.com/zju-vipa/TransferbilityFromAttributionMaps.
