Previous adversarial domain alignment methods for unsupervised domain adaptation (UDA) pursue conditional domain alignment via intermediate pseudo labels. However, these pseudo labels are generated by independent instances without considering the global data structure and tend to be noisy, making them unreliable for adversarial domain adaptation. Compared with pseudo labels, prototypes are more reliable to represent the data structure resistant to the domain shift since they are summarized over all the relevant instances. In this work, we attempt to calibrate the noisy pseudo labels with prototypes. Specifically, we first obtain a reliable prototypical representation for each instance by multiplying the soft instance predictions with the global prototypes. Based on the prototypical representation, we propose a novel Prototypical Adversarial Learning (PAL) scheme and exploit it to align both feature representations and intermediate prototypes across domains. Besides, with the intermediate prototypes as a proxy, we further minimize the intra-class variance in the target domain to adaptively improve the pseudo labels. Integrating the three objectives, we develop an unified framework termed PrototypicAl uNsupervised Domain Adaptation (PANDA) for UDA. Experiments show that PANDA achieves state-of-the-art or competitive results on multiple UDA benchmarks including both object recognition and semantic segmentation tasks.

本研究提出了牵扯到无监督领域自适应的条件对抗训练算法。实验证明基于我们提出的规范化输出的条件方法能够成功地将多模态结构进行对齐，甚至能够超越现有的领域对抗训练方法。同时，将基于原型的条件与规范化输出结合使用可以显著改善适应性性能。

基于原型的标准化输出调节器的对抗域适应