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.

本文提出了一个用于领域自适应人员重新识别的多专家头脑风暴网络（MEB-Net），采用相互学习策略，通过多个具有不同体系结构的网络专家模型进行预训练，在专家模型之间进行头脑风暴，以适应领域，增强适应的重新识别模型的区分能力，并通过引入关于专家权威性的正则化方案来容纳具有不同体系结构的专家模型的异质性。通过对大规模数据集（Market-1501 和 DukeMTMC-reID）进行的广泛实验表明，MEB-Net 比现有技术具有更优秀的表现。