A Unified Framework for Deep Reconstruction Enhancement and Anomaly Detection
Amelia ODonnell,
Department of Computer Science, Technical University of Munich, Munich, Germany
Clara Simmons
Department of Computer Science, Technical University of Munich, Munich, Germany
Marcus Vance
Department of Computer Science, Technical University of Munich, Munich, Germany
Keywords: Anomaly Detection,, Deep Learning,, Generative Models,, Feature Reconstruction,, Optimization
Abstract
Anomaly detection in high-dimensional data streams remains a fundamental challenge in computer science, particularly when deploying robust machine learning systems in unpredictable real-world environments. Traditional unsupervised methods often struggle with a pervasive trade-off between accurately reconstructing normal data patterns and inadvertently over-reconstructing anomalous instances, which fundamentally degrades the distinctiveness of the anomaly score. In this paper, we propose a comprehensive unified framework for deep reconstruction enhancement and anomaly detection that mitigates these pathological memorization effects while preserving high fidelity for in-distribution representations. Our architecture introduces a novel dual-pathway feature enhancement module integrated with a multi-scale autoencoding backbone, which structurally constrains the latent space manifold to isolate and amplify reconstruction errors specifically for anomalous perturbations. By explicitly formulating a joint optimization objective that simultaneously maximizes representation quality for normal instances and enforces tight bounding around the nominal manifold, our approach achieves exceptional discriminative power. We conduct extensive empirical evaluations across multiple complex domains, demonstrating superior performance in standard metrics such as the area under the receiver operating characteristic curve. The proposed system effectively bridges the gap between generative fidelity and diagnostic sensitivity, establishing a new operational standard for automated defect detection, network intrusion monitoring, and medical image screening.
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