  Recent works of multi-source domain adaptation focus on learning a
domain-agnostic model, of which the parameters are static. However, such a
static model is difficult to handle conflicts across multiple domains, and
suffers from a performance degradation in both source domains and target
domain. In this paper, we present dynamic transfer to address domain conflicts,
where the model parameters are adapted to samples. The key insight is that
adapting model across domains is achieved via adapting model across samples.
Thus, it breaks down source domain barriers and turns multi-source domains into
a single-source domain. This also simplifies the alignment between source and
target domains, as it only requires the target domain to be aligned with any
part of the union of source domains. Furthermore, we find dynamic transfer can
be simply modeled by aggregating residual matrices and a static convolution
matrix. Experimental results show that, without using domain labels, our
dynamic transfer outperforms the state-of-the-art method by more than 3% on the
large multi-source domain adaptation datasets -- DomainNet. Source code is at
https://github.com/liyunsheng13/DRT.
