  Often the best performing deep neural models are ensembles of multiple
base-level networks, nevertheless, ensemble learning with respect to domain
adaptive person re-ID remains unexplored. In this paper, we propose a multiple
expert brainstorming network (MEB-Net) for domain adaptive person re-ID,
opening up a promising direction about model ensemble problem under
unsupervised conditions. MEB-Net adopts a mutual learning strategy, where
multiple networks with different architectures are pre-trained within a source
domain as expert models equipped with specific features and knowledge, while
the adaptation is then accomplished through brainstorming (mutual learning)
among expert models. MEB-Net accommodates the heterogeneity of experts learned
with different architectures and enhances discrimination capability of the
adapted re-ID model, by introducing a regularization scheme about authority of
experts. Extensive experiments on large-scale datasets (Market-1501 and
DukeMTMC-reID) demonstrate the superior performance of MEB-Net over the
state-of-the-arts.
