Difficult /= Effective: Meta-Learning of Effectiveness Hyperplanes Vector for Anomaly Generation Steering

Industrial anomaly detection relies heavily on synthetic anomalies to overcome the scarcity of real defects. However, visually plausible anomalies are not necessarily effective training samples: they may be redundant, overly easy, or uninformative to a downstream detector. We propose Effectiveness Hyperplane Steering Anomaly Generation that generates anomalies directly optimized for downstream training effectiveness. Our key idea is to learn a linear effectiveness classifier whose decision boundary defines an effectiveness hyperplane in diffusion feature space; the normal vector of this hyperplane points toward increasingly effective samples. The classifier is trained end-to-end by back-propagating real validation performance through meta-learning. Once learned, the steering vector guides a frozen diffusion model by direct feature addition at inference, without any detector evaluation or iterative refinement. The classifier can also score unseen samples on its own. Experiments demonstrate consistent gains across anomaly categories, yielding macro-average improvements of 6.04/9.27/11.49 points in pixel-level AUROC/AP/AUPRO and 5.15/4.15 points in image-level AUROC/AP on the Real-IAD dataset. The code will be public after acceptance.