Domain adversarial training has been ubiquitous for achieving invariant
representations and is used widely for various domain adaptation tasks. In
recent times, methods converging to smooth optima have shown improved
generalization for supervised learning tasks like classification. In this work,
we analyze the effect of smoothness enhancing formulations on domain
adversarial training, the objective of which is a combination of task loss (eg.
classification, regression, etc.) and adversarial terms. We find that
converging to a smooth minima with respect to (w.r.t.) task loss stabilizes the
adversarial training leading to better performance on target domain. In
contrast to task loss, our analysis shows that converging to smooth minima
w.r.t. adversarial loss leads to sub-optimal generalization on the target
domain. Based on the analysis, we introduce the Smooth Domain Adversarial
Training (SDAT) procedure, which effectively enhances the performance of
existing domain adversarial methods for both classification and object
detection tasks. Our analysis also provides insight into the extensive usage of
SGD over Adam in the community for domain adversarial training.

本文对平滑性增强公式对于领域对抗训练的影响进行了分析，发现通过对任务损失和对抗项的平滑最小值进行收敛来稳定对抗训练可以在目标领域获得更好的性能。作者介绍了 SDAT 过程，该过程有效地提高了现有领域对抗方法的性能，同时提供了 SGD 在领域对抗培训中广泛使用的洞见。