  In this work, we attempt to address unsupervised domain adaptation by
devising simple and compact conditional domain adversarial training methods. We
first revisit the simple concatenation conditioning strategy where features are
concatenated with output predictions as the input of the discriminator. We find
the concatenation strategy suffers from the weak conditioning strength. We
further demonstrate that enlarging the norm of concatenated predictions can
effectively energize the conditional domain alignment. Thus we improve
concatenation conditioning by normalizing the output predictions to have the
same norm of features, and term the derived method as Normalized OutpUt
coNditioner~(NOUN). However, conditioning on raw output predictions for domain
alignment, NOUN suffers from inaccurate predictions of the target domain. To
this end, we propose to condition the cross-domain feature alignment in the
prototype space rather than in the output space. Combining the novel
prototype-based conditioning with NOUN, we term the enhanced method as
PROtotype-based Normalized OutpUt coNditioner~(PRONOUN). Experiments on both
object recognition and semantic segmentation show that NOUN can effectively
align the multi-modal structures across domains and even outperform
state-of-the-art domain adversarial training methods. Together with
prototype-based conditioning, PRONOUN further improves the adaptation
performance over NOUN on multiple object recognition benchmarks for UDA.
