J Shanghai Jiaotong Univ Sci ›› 2023, Vol. 28 ›› Issue (6): 703-716.doi: 10.1007/s12204-022-2487-5
张晟嘉1,2,林天成1,2,徐奕1,2
接受日期:
2021-07-23
出版日期:
2023-11-28
发布日期:
2023-12-04
ZHANG Shengjia(张晟嘉),LIN Tiancheng(林天成),XU Yi*(徐奕)
Accepted:
2021-07-23
Online:
2023-11-28
Published:
2023-12-04
摘要: 无监督域适应利用标签完整的源域数据,通过显式的数据分布差异最小化或对抗学习,提高无标签目标域的分类性能。作为一种增强方法,在域适应过程中会涉及类别对齐,即利用模型预测来加强目标特征识别。此方法存在两个问题:在目标域中,错误的类别预测会导致伪标签不准确;在源域中,过拟合会导致分布偏差。因此本文提出了一种与模型无关的两阶段学习框架,利用软伪标签策略大大减少了错误的模型预测,并利用课程学习策略避免了源域的过拟合。理论上,成功降低目标域预期误差上限的综合风险。在第一阶段,我们使用基于分布对齐的无监督域适应方法训练模型,以获得置信度相当高的目标域软语义标签。为了避免源域的过拟合,在第二阶段,我们提出了一种课程学习策略,以自适应性地控制两个域损失之间的权重,从而使训练阶段的重点逐渐从源域分布转移到目标域分布,并提高目标域的预测置信度。在两个常见基准数据集上进行的广泛实验验证了我们提出的框架在提升排名靠前的无监督域适应算法性能方面的普遍有效性,并证明了其一贯的卓越性能。
中图分类号:
张晟嘉, 林天成, 徐奕, . 利用软伪标签促进无监督领域自适应与课程学习[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(6): 703-716.
ZHANG Shengjia(张晟嘉), LIN Tiancheng(林天成), XU Yi(徐奕). Boosting Unsupervised Domain Adaptation with Soft Pseudo-Label and Curriculum Learning[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(6): 703-716.
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