  In this work, we present a novel meta-learning algorithm, i.e. TTNet, that
regresses model parameters for novel tasks for which no ground truth is
available (zero-shot tasks). In order to adapt to novel zero-shot tasks, our
meta-learner learns from the model parameters of known tasks (with ground
truth) and the correlation of known tasks to zero-shot tasks. Such intuition
finds its foothold in cognitive science, where a subject (human baby) can adapt
to a novel-concept (depth understanding) by correlating it with old concepts
(hand movement or self-motion), without receiving explicit supervision. We
evaluated our model on the Taskonomy dataset, with four tasks as zero-shot:
surface-normal, room layout, depth, and camera pose estimation. These tasks
were chosen based on the data acquisition complexity and the complexity
associated with the learning process using a deep network. Our proposed
methodology out-performs state-of-the-art models (which use ground truth)on
each of our zero-shot tasks, showing promise on zero-shot task transfer. We
also conducted extensive experiments to study the various choices of our
methodology, as well as showed how the proposed method can also be used in
transfer learning. To the best of our knowledge, this is the firstsuch effort
on zero-shot learning in the task space.
