  Neural Architecture Search (NAS) is a collection of methods to craft the way
neural networks are built. Current NAS methods are far from ab initio and
automatic, as they use manual backbone architectures or micro building blocks
(cells), which have had minor breakthroughs in performance compared to random
baselines. They also involve a significant manual expert effort in various
components of the NAS pipeline. This raises a natural question - Are the
current NAS methods still heavily dependent on manual effort in the search
space design and wiring like it was done when building models before the advent
of NAS? In this paper, instead of merely chasing slight improvements over
state-of-the-art (SOTA) performance, we revisit the fundamental approach to NAS
and propose a novel approach called ReNAS that can search for the complete
neural network without much human effort and is a step closer towards
AutoML-nirvana. Our method starts from a complete graph mapped to a neural
network and searches for the connections and operations by balancing the
exploration and exploitation of the search space. The results are on-par with
the SOTA performance with methods that leverage handcrafted blocks. We believe
that this approach may lead to newer NAS strategies for a variety of network
types.
