Page 136 - 《软件学报》2024年第4期

1714 软件学报 2024 年第 35 卷第 4 期

模型精度(%) 模型精度(%)

超参数α 超参数β
(a) (b)
图 3 参数敏感性分析

4 结 论

本文解决一个更加实际且更具挑战性的任务——多源部分域适应, 并提出对应的学习框架(AW-MSPDA).
该场景下, 源域往往包含丰富的信息, 目标域特征可能由多个源域特征表出, 我们使用多样性特征提取方式
来构建模型, 同时提出域级别的自适应权重来区分不同源域对目标域的贡献. 为了有效实现源域知识的迁移,
我们提出了多层次的对齐方式来促进正迁移. 此外, 我们利用目标域伪标签设计类级别的自适应权重, 来对
分类器进行加权以过滤源域中的无关类样本. 我们通过实验, 在多源域适应和多源部分域适应场景下证明了
该方法的有效性. 在未来, 我们将探索更精确的过滤算法以排除源域中的无关类样本, 从而实现更好的域适
应性能.

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