  Domain adaptation enables the learner to safely generalize into novel
environments by mitigating domain shifts across distributions. Previous works
may not effectively uncover the underlying reasons that would lead to the
drastic model degradation on the target task. In this paper, we empirically
reveal that the erratic discrimination of the target domain mainly stems from
its much smaller feature norms with respect to that of the source domain. To
this end, we propose a novel parameter-free Adaptive Feature Norm approach. We
demonstrate that progressively adapting the feature norms of the two domains to
a large range of values can result in significant transfer gains, implying that
those task-specific features with larger norms are more transferable. Our
method successfully unifies the computation of both standard and partial domain
adaptation with more robustness against the negative transfer issue. Without
bells and whistles but a few lines of code, our method substantially lifts the
performance on the target task and exceeds state-of-the-arts by a large margin
(11.5% on Office-Home and 17.1% on VisDA2017). We hope our simple yet effective
approach will shed some light on the future research of transfer learning. Code
is available at https://github.com/jihanyang/AFN.
