  Adversarial learning strategy has demonstrated remarkable performance in
dealing with single-source Domain Adaptation (DA) problems, and it has recently
been applied to Multi-source DA (MDA) problems. Although most existing MDA
strategies rely on a multiple domain discriminator setting, its effect on the
latent space representations has been poorly understood. Here we adopt an
information-theoretic approach to identify and resolve the potential adverse
effect of the multiple domain discriminators on MDA: disintegration of
domain-discriminative information, limited computational scalability, and a
large variance in the gradient of the loss during training. We examine the
above issues by situating adversarial DA in the context of information
regularization. This also provides a theoretical justification for using a
single and unified domain discriminator. Based on this idea, we implement a
novel neural architecture called a Multi-source Information-regularized
Adaptation Networks (MIAN). Large-scale experiments demonstrate that MIAN,
despite its structural simplicity, reliably and significantly outperforms other
state-of-the-art methods.
