用公共数据生成弱标签，提升私有表格数据的异常检测效果
Tabular Data Adapters: Improving Outlier Detection for Unlabeled Private Data
- 通过共享自编码器转换私有数据，匹配公开模型输入格式
- 在50个不同领域数据集上，标签准确率高于基线方法
- 解决工业场景中无标签数据的冷启动问题，适合实际部署
深度学习的成功通常基于大型公开数据集，但在应用于内部私有数据时，常面临数据结构差异、领域偏移及缺乏标签的问题。本文提出表格数据适配器（TDA），一种为无标签表格数据生成软标签的新方法，用于异常检测任务。通过识别统计相似的公开数据集，并利用共享自编码器将私有数据转换为与先进公开模型兼容的格式，该方法可生成弱标签，从而缓解标注的冷启动难题。在50个跨领域的表格数据集上的实验表明，该方法不仅能提供比基线更准确的标注，还显著降低计算时间。本方案为连接公开研究模型与真实工业应用提供了可扩展、高效且低成本的解决方案。
原文摘要 · Abstract (English)
The remarkable success of Deep Learning approaches is often based and demonstrated on large public datasets. However, when applying such approaches to internal, private datasets, one frequently faces challenges arising from structural differences in the datasets, domain shift, and the lack of labels. In this work, we introduce Tabular Data Adapters (TDA), a novel method for generating soft labels for unlabeled tabular data in outlier detection tasks. By identifying statistically similar public datasets and transforming private data (based on a shared autoencoder) into a format compatible with state-of-the-art public models, our approach enables the generation of weak labels. It thereby can help to mitigate the cold start problem of labeling by basing on existing outlier detection models for public datasets. In experiments on 50 tabular datasets across different domains, we demonstrate that our method is able to provide more accurate annotations than baseline approaches while reducing computational time. Our approach offers a scalable, efficient, and cost-effective solution, to bridge the gap between public research models and real-world industrial applications.
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