An Approach for Efficient Neural Architecture Search Space Definition
Léo Pouy Fouad Khenfri Patrick Leserf Chokri Mraidha Cherif Larouci
As we advance in the fast-growing era of Machine Learning, various new and
more complex neural architectures are arising to tackle problem more
efficiently. On the one hand their efficient usage requires advanced knowledge
and expertise, which is most of the time difficult to find on the labor market.
On the other hand, searching for an optimized neural architecture is a
time-consuming task when it is performed manually using a trial and error
approach. Hence, a method and a tool support is needed to assist users of
neural architectures, leading to an eagerness in the field of Automatic Machine
Learning (AutoML). When it comes to Deep Learning, an important part of AutoML
is the Neural Architecture Search (NAS). In this paper, we propose a novel
cell-based hierarchical search space, easy to comprehend and manipulate. The
objectives of the proposed approach are to optimize the search-time and to be
general enough to handle most of state of the art Convolutional Neural Networks
(CNN) architectures.