  Reducing feature redundancy has shown beneficial effects for improving the
accuracy of deep learning models, thus it is also indispensable for the models
of unsupervised domain adaptation (UDA). Nevertheless, most recent efforts in
the field of UDA ignores this point. Moreover, main schemes realizing this in
general independent of UDA purely involve a single domain, thus might not be
effective for cross-domain tasks. In this paper, we emphasize the significance
of reducing feature redundancy for improving UDA in a bi-level way. For the
first level, we try to ensure compact domain-specific features with a
transferable decorrelated normalization module, which preserves specific domain
information whilst easing the side effect of feature redundancy on the sequel
domain-invariance. In the second level, domain-invariant feature redundancy
caused by domain-shared representation is further mitigated via an alternative
brand orthogonality for better generalization. These two novel aspects can be
easily plugged into any BN-based backbone neural networks. Specifically, simply
applying them to ResNet50 has achieved competitive performance to the
state-of-the-arts on five popular benchmarks. Our code will be available at
https://github.com/dreamkily/gUDA.
