为自编码器异常检测提供统计可解释性，解决域适应后误报率失控问题。
Statistical Inference for Autoencoder-based Anomaly Detection after Representation Learning-based Domain Adaptation
- 基于选择性推断框架，为异常检测结果生成有效p值。
- 在真实与合成数据上实现0.05以下的误报率控制。
- 支持大规模深度模型，显著提升计算效率，适合工业级应用。
异常检测在众多领域中至关重要，但目标域数据有限时性能会下降。域适应（DA）通过从数据丰富的源域迁移知识来缓解此问题，但适应过程会引入额外不确定性，难以对结果做出统计有效的推断。本文提出STAND-DA——一种基于表示学习的域适应后、自编码器异常检测的统计严格框架。该框架基于选择性推断（SI），可计算检测到的异常的有效p值，并严格将假阳性率控制在预设水平α（如0.05）以下。为应对将SI应用于深度学习模型时的计算挑战，我们开发了GPU加速的SI实现，显著提升了可扩展性和运行效率。在合成与真实世界数据集上的大量实验验证了所提方法的理论正确性与计算高效性。
原文摘要 · Abstract (English)
Anomaly detection (AD) plays a vital role across a wide range of domains, but its performance might deteriorate when applied to target domains with limited data. Domain Adaptation (DA) offers a solution by transferring knowledge from a related source domain with abundant data. However, this adaptation process can introduce additional uncertainty, making it difficult to draw statistically valid conclusions from AD results. In this paper, we propose STAND-DA -- a novel framework for statistically rigorous Autoencoder-based AD after Representation Learning-based DA. Built on the Selective Inference (SI) framework, STAND-DA computes valid $p$-values for detected anomalies and rigorously controls the false positive rate below a pre-specified level $α$ (e.g., 0.05). To address the computational challenges of applying SI to deep learning models, we develop the GPU-accelerated SI implementation, significantly enhancing both scalability and runtime performance. This advancement makes SI practically feasible for modern, large-scale deep architectures. Extensive experiments on synthetic and real-world datasets validate the theoretical results and computational efficiency of the proposed STAND-DA method.
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