计算机科学
领域(数学分析)
杠杆(统计)
域适应
成对比较
人工智能
业务领域
模式识别(心理学)
数学
分类器(UML)
数学分析
业务流程
地球化学
相容性(地球化学)
地质学
业务架构
作者
Xian Zhao,Lei Huang,Jie Nie,Zhengang Wei
标识
DOI:10.1109/tmm.2023.3330088
摘要
Unsupervised domain adaptation (UDA) involves the transfer of knowledge from a labelled source domain to an unlabelled target domain. Recent studies have introduced the concept of intermediate domains to handle significant domain discrepancies between source and target domains. Constructing an appropriate intermediate domain is a crucial step in handling scenarios with substantial domain differences. In this paper, we propose a novel progressive UDA method called adaptive multi-scale intermediate domain via progressive training (AMPT), which has achieved remarkable effectiveness in alleviating large discrepancies between domains. We design a multi-scale similarity metrics module to solve the issue of different scales across different domains by simultaneously computing the pairwise distance between source domain images and the target domain images on multiple scales. Furthermore, we explore a progressive training strategy that facilitates smooth adaptation of the source domain to the target domain by utilizing the intermediate domain. During the progressive training process, considering a positive feedback mechanism, we iteratively leverage task losses and distillation loss through a dynamic threshold. This ensures that the trained intermediate domain branch progressively constrains the target domain branch, and the intermediate domain can be generated dynamically, leading to a smoother gradual adaptation from the source domain to the target domain. Extensive experimental results demonstrate the superiority of our proposed method AMPT on well-known UDA datasets, including Office-Home, Office-31 and DomainNet.
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