This paper proposes a novel unsupervised domain adaption (UDA) method based on contrastive bi-projector (CBP), which can improve the existing UDA methods. It is called CBPUDA here, which effectively promotes the feature extractors (FEs) to reduce the generation of ambiguous features for classification and domain adaption. The CBP differs from traditional bi-classifier-based methods at that these two classifiers are replaced with two projectors of performing a mapping from the input feature to two distinct features. These two projectors and the FEs in the CBPUDA can be trained adversarially to obtain more refined decision boundaries so that it can possess powerful classification performance. Two properties of the proposed loss function are analyzed here. The first property is to derive an upper bound of joint prediction entropy, which is used to form the proposed loss function, contrastive discrepancy (CD) loss. The CD loss takes the advantages of the contrastive learning and the bi-classifier. The second property is to analyze the gradient of the CD loss and then overcome the drawback of the CD loss. The result of the second property is utilized in the development of the gradient scaling (GS) scheme in this paper. The GS scheme can be exploited to tackle the unstable problem of the CD loss because training the CBPUDA requires using contrastive learning and adversarial learning at the same time. Therefore, using the CD loss with the GS scheme overcomes the problem mentioned above to make features more compact for intra-class and distinguishable for inter-class. Experimental results express that the CBPUDA is superior to conventional UDA methods under consideration in this paper for UDA and fine-grained UDA tasks.

翻译：摘要：本文提出了一种基于对比双投影器（CBP）的无监督域适应（UDA）新方法，能够改进现有UDA方法。该方法称为CBPUDA，可有效促进特征提取器（FEs）减少分类与域适应过程中模糊特征的生成。与传统基于双分类器的方法不同，CBP将两个分类器替换为两个投影器，用于将输入特征映射为两种不同的特征表示。在CBPUDA中，这两个投影器与特征提取器可通过对抗性训练获得更精细的决策边界，从而具备强大的分类性能。本文分析了所提损失函数的两个性质。第一个性质推导了联合预测熵的上界，并基于此构建了所提损失函数——对比差异（CD）损失。该损失兼具对比学习与双分类器的优势。第二个性质分析了CD损失的梯度，进而克服其缺陷。基于第二个性质的结论，本文进一步设计了梯度缩放（GS）方案。该方案可解决CD损失的不稳定问题，因为CBPUDA的训练需同时使用对比学习与对抗学习。因此，结合GS方案的CD损失能够克服上述问题，使特征在类内更紧凑、类间更可区分。实验结果表明，在UDA与细粒度UDA任务中，CBPUDA优于本文所考虑的传统UDA方法。