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：本文提出了一种基于对比双投影器（CBP）的新型无监督域自适应（UDA）方法，可有效提升现有UDA方法的性能。该方法命名为CBPUDA，通过促进特征提取器（FE）减少分类与域自适应过程中模糊特征的生成。CBP与传统基于双分类器方法的区别在于：它将两个分类器替换为两个投影器，负责将输入特征映射为两种不同的特征表示。CBPUDA中的两个投影器与特征提取器可通过对抗训练获得更精细的决策边界，从而具备强大的分类性能。本文分析了所提出损失函数的两个特性：第一个特性是推导联合预测熵的上界，并据此构建对比差异（CD）损失函数，该损失同时融合了对比学习与双分类器的优势；第二个特性是分析CD损失的梯度性质以克服其缺陷，并基于此研究梯度缩放（GS）方案。由于CBPUDA训练需要同时使用对比学习与对抗学习，GS方案可有效解决CD损失训练不稳定的问题。因此，采用结合GS方案的CD损失能够使类内特征更紧凑、类间特征更易区分，从而克服上述问题。实验结果表明，在标准UDA与细粒度UDA任务中，CBPUDA的性能均优于本文所对比的传统UDA方法。