Most domain adaptation (DA) methods are based on either a convolutional
neural networks (CNNs) or a vision transformers (ViTs). They align the
distribution differences between domains as encoders without considering their
unique characteristics. For instance, ViT excels in accuracy due to its
superior ability to capture global representations, while CNN has an advantage
in capturing local representations. This fact has led us to design a hybrid
method to fully take advantage of both ViT and CNN, called Explicitly
Class-specific Boundaries (ECB). ECB learns CNN on ViT to combine their
distinct strengths. In particular, we leverage ViT's properties to explicitly
find class-specific decision boundaries by maximizing the discrepancy between
the outputs of the two classifiers to detect target samples far from the source
support. In contrast, the CNN encoder clusters target features based on the
previously defined class-specific boundaries by minimizing the discrepancy
between the probabilities of the two classifiers. Finally, ViT and CNN mutually
exchange knowledge to improve the quality of pseudo labels and reduce the
knowledge discrepancies of these models. Compared to conventional DA methods,
our ECB achieves superior performance, which verifies its effectiveness in this
hybrid model. The project website can be found
this https URL

通过设计一种名为明确类别边界 (ECB) 的混合方法，我们充分利用了 ViT 和 CNN 的独特优势，其中 ECB 利用 ViT 的特性来显式地找到特定类别的决策边界，同时 CNN 通过最小化两个分类器的概率之差来对目标特征进行聚类，最终 ViT 和 CNN 相互交换知识以提高伪标签的质量和减小模型间的知识差异，相较于传统的领域自适应方法，我们的 ECB 方法取得了卓越的性能。