A Novel Statistical Measure for Out-of-Distribution Detection in Data Quality Assurance
This addresses data quality issues for AI security, but it appears incremental as it builds on existing OOD detection methods with a new statistical approach.
The paper tackled the problem of detecting out-of-distribution data in AI quality management by proposing a novel statistical measure based on deep learning features, achieving feasibility and effectiveness validated through experiments on benchmark and industrial datasets.
Data outside the problem domain poses significant threats to the security of AI-based intelligent systems. Aiming to investigate the data domain and out-of-distribution (OOD) data in AI quality management (AIQM) study, this paper proposes to use deep learning techniques for feature representation and develop a novel statistical measure for OOD detection. First, to extract low-dimensional representative features distinguishing normal and OOD data, the proposed research combines the deep auto-encoder (AE) architecture and neuron activation status for feature engineering. Then, using local conditional probability (LCP) in data reconstruction, a novel and superior statistical measure is developed to calculate the score of OOD detection. Experiments and evaluations are conducted on image benchmark datasets and an industrial dataset. Through comparative analysis with other common statistical measures in OOD detection, the proposed research is validated as feasible and effective in OOD and AIQM studies.