We consider multi-source free domain adaptation, the problem of adapting
multiple existing models to a new domain without accessing the source data.
Among existing approaches, methods based on model ensemble are effective in
both the source and target domains, but incur significantly increased
computational costs. Towards this dilemma, in this work, we propose a novel
framework called SepRep-Net, which tackles multi-source free domain adaptation
via model Separation and Reparameterization.Concretely, SepRep-Net reassembled
multiple existing models to a unified network, while maintaining separate
pathways (Separation). During training, separate pathways are optimized in
parallel with the information exchange regularly performed via an additional
feature merging unit. With our specific design, these pathways can be further
reparameterized into a single one to facilitate inference (Reparameterization).
SepRep-Net is characterized by 1) effectiveness: competitive performance on the
target domain, 2) efficiency: low computational costs, and 3) generalizability:
maintaining more source knowledge than existing solutions. As a general
approach, SepRep-Net can be seamlessly plugged into various methods. Extensive
experiments validate the performance of SepRep-Net on mainstream benchmarks.

通过模型分离和参数化，我们提出了一种名为 SepRep-Net 的新框架，用于解决多源自由域自适应问题，在保持计算成本较低的同时，在目标领域上获得竞争性性能，同时保留比现有解决方案更多的源知识。