针对时间序列分类中的领域偏移问题，提出一种基于动量编码器的半监督域适应框架。
MoSSDA: A Semi-Supervised Domain Adaptation Framework for Multivariate Time-Series Classification using Momentum Encoder
- 采用双阶段动量编码器，分离梯度流以学习更丰富的特征表示。
- 在六个数据集上表现优于现有方法，尤其在少量标注目标数据下提升显著。
- 无需数据增强，适合工业时间序列等标注成本高的场景。
深度学习在多个领域表现优异，但当训练与测试数据分布不同时（领域偏移），模型性能会下降。半监督域适应（SSDA）假设源域有完整标签，而目标域仅部分标签可用。本文提出一种新型两阶段动量编码器驱动的SSDA框架MoSSDA，用于多变量时间序列分类。时间序列对噪声敏感，序列依赖性导致领域偏移，造成性能严重下降。为获得鲁棒、领域不变且类别可区分的表征，MoSSDA使用领域不变编码器联合学习源域和目标域特征，并将学习到的特征输入由在线动量编码器构成的mixup增强正对比模块。最终分类器利用有限标注的目标域数据训练，实现一致性和判别性。通过分离编码器与分类器之间的梯度流，实现两阶段训练，获取丰富复杂的表示。在六个不同数据集上的大量实验表明，MoSSDA在三种不同主干网络及多种未标记目标数据比例下均达到当前最优性能。消融实验证明每个模块（包括两阶段学习）均有效。代码已开源：https://github.com/seonyoungKimm/MoSSDA
原文摘要 · Abstract (English)
Deep learning has emerged as the most promising approach in various fields; however, when the distributions of training and test data are different (domain shift), the performance of deep learning models can degrade. Semi-supervised domain adaptation (SSDA) is a major approach for addressing this issue, assuming that a fully labeled training set (source domain) is available, but the test set (target domain) provides labels only for a small subset. In this study, we propose a novel two-step momentum encoder-utilized SSDA framework, MoSSDA, for multivariate time-series classification. Time series data are highly sensitive to noise, and sequential dependencies cause domain shifts resulting in critical performance degradation. To obtain a robust, domain-invariant and class-discriminative representation, MoSSDA employs a domain-invariant encoder to learn features from both source and target domains. Subsequently, the learned features are fed to a mixup-enhanced positive contrastive module consisting of an online momentum encoder. The final classifier is trained with learned features that exhibit consistency and discriminability with limited labeled target domain data, without data augmentation. We applied a two-stage process by separating the gradient flow between the encoders and the classifier to obtain rich and complex representations. Through extensive experiments on six diverse datasets, MoSSDA achieved state-of-the-art performance for three different backbones and various unlabeled ratios in the target domain data. The Ablation study confirms that each module, including two-stage learning, is effective in improving the performance. Our code is available at https://github.com/seonyoungKimm/MoSSDA
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