Deep models have achieved prominent results in pattern recognition tasks, especially computer vision and natural language processing. However, the dataset bias caused by the distribution discrepancy between the training and testing data hinders the generalization ability of deep models. Though many domain adaptation approaches have been proposed to mitigate such negative effect, most of them improve the transferability of features by aligning global distributions of deep models. Few researchers pay attention to the versatility of deep features which can play a vital role in cross-domain recognition. In this paper, we propose to enrich the classic deep learning models by capturing high-low-frequency information and multi-scale features, which deal with the domain shift that cannot be easily addressed by merely feature-level alignment. The Hierarchical Transfer Network (HTN) leverages octave convolution, pyramid features, and self-attention mechanism for revamping the classic models, which can be further integrated with any domain alignment approaches by replacing the feature extractor with the proposed HTN. Extensive experiments have been conducted on three public domain adaptation benchmarks. The results show that the proposed HTN can effectively improve adversarial-based, statistics-based, and norm-based domain adaptation approaches, achieving competitive performance without involving model complexity.

深度模型在模式识别任务中取得了突出的成果，尤其是计算机视觉和自然语言处理。然而，训练和测试数据之间的分布差异导致的数据集偏差阻碍了深度模型的泛化能力。尽管已经提出了许多领域适应方法来减轻这种负面影响，但大多数方法通过对齐深度模型的全局分布来提高特征的可转移性。很少有研究人员关注可以在跨域识别中发挥重要作用的深度特征的多功能性。在本文中，我们建议通过捕获高频低频信息和多尺度特征来丰富经典的深度学习模型，这些特征处理仅通过特征级对齐无法轻易解决的域转移。分层传输网络 (HTN) 利用八度卷积、金字塔特征和自注意力机制来改进经典模型，通过用提议的 HTN 替换特征提取器，可以进一步与任何域对齐方法集成。已经对三个公共领域适应基准进行了广泛的实验。结果表明，所提出的 HTN 可以有效地改进基于对抗性、基于统计和基于规范的域适应方法，在不涉及模型复杂性的情况下实现有竞争力的性能。已经对三个公共领域适应基准进行了广泛的实验。结果表明，所提出的 HTN 可以有效地改进基于对抗性、基于统计和基于规范的域适应方法，在不涉及模型复杂性的情况下实现有竞争力的性能。已经对三个公共领域适应基准进行了广泛的实验。结果表明，所提出的 HTN 可以有效地改进基于对抗性、基于统计和基于规范的域适应方法，在不涉及模型复杂性的情况下实现有竞争力的性能。