  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.
