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.

本研究解决了将深度学习应用于内部私有数据时面临的异常检测挑战，如数据结构差异和缺乏标签。我们提出了一种名为表格数据适配器（TDA）的新方法，通过识别统计上相似的公共数据集并将私有数据转换为兼容格式，生成弱标签，从而在50个不同领域的数据集上实现了更准确的注释，并减少了计算时间，为公共模型与实际应用之间架起了桥梁。

表格数据适配器：改善无标签私有数据的异常检测