  The design of handcrafted neural networks requires a lot of time and
resources. Recent techniques in Neural Architecture Search (NAS) have proven to
be competitive or better than traditional handcrafted design, although they
require domain knowledge and have generally used limited search spaces. In this
paper, we propose a novel framework for neural architecture search, utilizing a
dictionary of models of base tasks and the similarity between the target task
and the atoms of the dictionary; hence, generating an adaptive search space
based on the base models of the dictionary. By introducing a gradient-based
search algorithm, we can evaluate and discover the best architecture in the
search space without fully training the networks. The experimental results show
the efficacy of our proposed task-aware approach.
