  Adapting visual object detectors to operational target domains is a
challenging task, commonly achieved using unsupervised domain adaptation (UDA)
methods. Recent studies have shown that when the labeled dataset comes from
multiple source domains, treating them as separate domains and performing a
multi-source domain adaptation (MSDA) improves the accuracy and robustness over
blending these source domains and performing a UDA. For adaptation, existing
MSDA methods learn domain-invariant and domain-specific parameters (for each
source domain). However, unlike single-source UDA methods, learning
domain-specific parameters makes them grow significantly in proportion to the
number of source domains. This paper proposes a novel MSDA method called
Prototype-based Mean Teacher (PMT), which uses class prototypes instead of
domain-specific subnets to encode domain-specific information. These prototypes
are learned using a contrastive loss, aligning the same categories across
domains and separating different categories far apart. Given the use of
prototypes, the number of parameters required for our PMT method does not
increase significantly with the number of source domains, thus reducing memory
issues and possible overfitting. Empirical studies indicate that PMT
outperforms state-of-the-art MSDA methods on several challenging object
detection datasets. Our code is available at
https://github.com/imatif17/Prototype-Mean-Teacher.
