用最优传输距离提升少量标注图像的分类效果，用于卫星信号多路径干扰检测。
Wasserstein distance based semi-supervised manifold learning and application to GNSS multi-path detection
- 以沃尔德斯坦距离度量图像相似性，实现半监督图传播学习
- 在特定超参数下，分类准确率显著优于全监督方法
- 适合标注数据稀缺的高精度信号处理场景
本研究提出一种基于最优传输的半监督方法，利用深度卷积网络从少量标注图像数据中学习。核心思想是基于隐式图的归纳式半监督学习，样本间相似性采用沃尔德斯坦距离衡量，并用于标签传播机制。该方法应用于真实场景下的GNSS多路径干扰检测问题，在多种信号条件下进行实验。结果表明，在特定超参数组合下（控制半监督程度与度量敏感度），分类准确率显著高于全监督训练方法。
原文摘要 · Abstract (English)
The main objective of this study is to propose an optimal transport based semi-supervised approach to learn from scarce labelled image data using deep convolutional networks. The principle lies in implicit graph-based transductive semi-supervised learning where the similarity metric between image samples is the Wasserstein distance. This metric is used in the label propagation mechanism during learning. We apply and demonstrate the effectiveness of the method on a GNSS real life application. More specifically, we address the problem of multi-path interference detection. Experiments are conducted under various signal conditions. The results show that for specific choices of hyperparameters controlling the amount of semi-supervision and the level of sensitivity to the metric, the classification accuracy can be significantly improved over the fully supervised training method.
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