  The performance of Multi-Source Unsupervised Domain Adaptation depends
significantly on the effectiveness of transfer from labeled source domain
samples. In this paper, we proposed an adversarial agent that learns a dynamic
curriculum for source samples, called Curriculum Manager for Source Selection
(CMSS). The Curriculum Manager, an independent network module, constantly
updates the curriculum during training, and iteratively learns which domains or
samples are best suited for aligning to the target. The intuition behind this
is to force the Curriculum Manager to constantly re-measure the transferability
of latent domains over time to adversarially raise the error rate of the domain
discriminator. CMSS does not require any knowledge of the domain labels, yet it
outperforms other methods on four well-known benchmarks by significant margins.
We also provide interpretable results that shed light on the proposed method.
