  Deep learning methods have become very successful at solving many complex
tasks such as image classification and segmentation, speech recognition and
machine translation. Nevertheless, manually designing a neural network for a
specific problem is very difficult and time-consuming due to the massive
hyperparameter search space, long training times, and lack of technical
guidelines for the hyperparameter selection. Moreover, most networks are highly
complex, task specific and over-parametrized. Recently, multiobjective neural
architecture search (NAS) methods have been proposed to automate the design of
accurate and efficient architectures. However, they only optimize either the
macro- or micro-structure of the architecture requiring the unset
hyperparameters to be manually defined, and do not use the information produced
during the optimization process to increase the efficiency of the search. In
this work, we propose EMONAS, an Efficient MultiObjective Neural Architecture
Search framework for the automatic design of neural architectures while
optimizing the network's accuracy and size. EMONAS is composed of a search
space that considers both the macro- and micro-structure of the architecture,
and a surrogate-assisted multiobjective evolutionary based algorithm that
efficiently searches for the best hyperparameters using a Random Forest
surrogate and guiding selection probabilities. EMONAS is evaluated on the task
of 3D cardiac segmentation from the MICCAI ACDC challenge, which is crucial for
disease diagnosis, risk evaluation, and therapy decision. The architecture
found with EMONAS is ranked within the top 10 submissions of the challenge in
all evaluation metrics, performing better or comparable to other approaches
while reducing the search time by more than 50% and having considerably fewer
number of parameters.
