  Despite remarkable progress achieved, most neural architecture search (NAS)
methods focus on searching for one single accurate and robust architecture. To
further build models with better generalization capability and performance,
model ensemble is usually adopted and performs better than stand-alone models.
Inspired by the merits of model ensemble, we propose to search for multiple
diverse models simultaneously as an alternative way to find powerful models.
Searching for ensembles is non-trivial and has two key challenges: enlarged
search space and potentially more complexity for the searched model. In this
paper, we propose a one-shot neural ensemble architecture search (NEAS)
solution that addresses the two challenges. For the first challenge, we
introduce a novel diversity-based metric to guide search space shrinking,
considering both the potentiality and diversity of candidate operators. For the
second challenge, we enable a new search dimension to learn layer sharing among
different models for efficiency purposes. The experiments on ImageNet clearly
demonstrate that our solution can improve the supernet's capacity of ranking
ensemble architectures, and further lead to better search results. The
discovered architectures achieve superior performance compared with
state-of-the-arts such as MobileNetV3 and EfficientNet families under aligned
settings. Moreover, we evaluate the generalization ability and robustness of
our searched architecture on the COCO detection benchmark and achieve a 3.1%
improvement on AP compared with MobileNetV3. Codes and models are available at
https://github.com/researchmm/NEAS.
