DeCoFlow: Structural Decomposition of Normalizing Flows for Continual Anomaly Detection

In industrial environments, new product categories arrive se-quentially, requiring continual anomaly detection without access to pastdata. Normalizing Flows (NFs) provide exact density estimation but suf-fer from catastrophic forgetting as parameter updates across tasks dis-tort the density manifold. While parameter isolation can prevent inter-ference, it must preserve the strict invertibility and Jacobian validityof NFs. To satisfy these requirements, we exploit the inherent propertythat affine coupling layers maintain transformation validity regardless ofsubnet parameterization. Based on this, we propose DeCoFlow, which de-composes subnets into a frozen universal base and task-specific low-rankadapters to isolate updates. We further introduce Task-Specific Align-ment, Auxiliary Coupling Layers, and Tail-Aware Loss to compensatefor frozen-base rigidity. DeCoFlow achieves state-of-the-art image-levelAUROCs of 98.40% on MVTec-AD and 93.00% on VisA, while maintain-ing parameter-level zero forgetting (0.00% FM under correct routing)with only 2.27M parameters per task.