  Recent works have proven that many relevant visual tasks are closely related
one to another. Yet, this connection is seldom deployed in practice due to the
lack of practical methodologies to transfer learned concepts across different
training processes. In this work, we introduce a novel adaptation framework
that can operate across both task and domains. Our framework learns to transfer
knowledge across tasks in a fully supervised domain (e.g., synthetic data) and
use this knowledge on a different domain where we have only partial supervision
(e.g., real data). Our proposal is complementary to existing domain adaptation
techniques and extends them to cross tasks scenarios providing additional
performance gains. We prove the effectiveness of our framework across two
challenging tasks (i.e., monocular depth estimation and semantic segmentation)
and four different domains (Synthia, Carla, Kitti, and Cityscapes).
