Embedded One-Class Classification for Deep Neural Network Representations

Abstract

Artificial neural networks (ANNs) make predictions based on patterns learned during training; however, their reliability may deteriorate when the data distribution or the trained model changes. This paper proposes an Embedded One-Class Classification (EOCC) framework for monitoring task-informed neural network representations. The predictive network is first trained using its original classification or regression objective and then fixed. Embedded Support Vector Data Description (ESVDD) or Embedded Least Squares Support Vector Data Description (ELS-SVDD) is subsequently fitted to embeddings extracted from a selected hidden layer. The framework is evaluated through classification and regression simulations involving mean, covariance, and mixed distributional shifts. Additional experiments examine embedding layer and activation choices, direct neural network weight perturbations, and model changes induced by altered training conditions. Comparisons with depth-based, density-based, covariance-based, isolation-based, and end-to-end deep one-class methods show that EOCC is competitive and frequently achieves low Type II error while maintaining the nominal in-control acceptance probability, although no method is uniformly superior across all settings. Illustrative applications involving the Internet Firewall, ELEC2, and SINE1 datasets demonstrate how the framework can identify changes reflected in neural network representations. The present framework performs change detection only; adaptation and automatic model updating remain directions for future research.