通过谱嵌入优化运输计划，提升跨域数据的表示一致性
Beyond Mapping : Domain-Invariant Representations via Spectral Embedding of Optimal Transport Plans
- 将平滑运输计划视为双分图邻接矩阵，用谱嵌入提取不变特征
- 在音乐类型识别、语音判别等任务中表现优于现有方法
- 适合处理训练与推理数据分布不一致的场景
训练与推理阶段的数据分布偏移仍是机器学习的核心挑战，常导致性能下降。为此，研究基于最优传输的无监督域适应方法，依赖运输计划近似莫尔格映射，但对正则化策略和超参数敏感，可能导致域对齐偏差。本文提出将平滑运输计划视为源域与目标域间的二分图邻接矩阵，通过谱嵌入获得域不变样本表示。我们在音乐流派识别、音乐-语音判别以及不同诊断场景下的电缆缺陷检测与分类任务上评估该方法，均取得整体优异性能。
原文摘要 · Abstract (English)
Distributional shifts between training and inference time data remain a central challenge in machine learning, often leading to poor performance. It motivated the study of principled approaches for domain alignment, such as optimal transport based unsupervised domain adaptation, that relies on approximating Monge map using transport plans, which is sensitive to the transport problem regularization strategy and hyperparameters, and might yield biased domains alignment. In this work, we propose to interpret smoothed transport plans as adjacency matrices of bipartite graphs connecting source to target domain and derive domain-invariant samples' representations through spectral embedding. We evaluate our approach on acoustic adaptation benchmarks for music genre recognition, music-speech discrimination, as well as electrical cable defect detection and classification tasks using time domain reflection in different diagnosis settings, achieving overall strong performances.
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