ADPretrain++: Dense Dual-Task Anomaly Representation Pretraining for Industrial Anomaly Detection

Unsupervised industrial anomaly detectors such as PatchCore, PaDiM and CFLOW score images and pixels with frozen features from generic backbones, which were never trained to separate normal from anomalous patterns. Residual anomaly-representation pretraining narrows this mismatch, but it optimises a single global objective on one feature space, even though image-level detection and pixel-level localisation require different evidence. We propose ADPretrain++, which pretrains residual features on an industrial dataset with real defect masks through a shared learnable-reference projector followed by an image adapter and a pixel adapter. The pixel adapter is trained with a dense norm-map loss and prototype shaping, and the image adapter with top-$k$ multiple-instance learning and descriptor shaping. The pretrained features replace the backbone features of an unchanged detector. With a DINOv3-L backbone and averaged over four benchmarks, the features raise PatchCore by 4.8 points of image-level area under the ROC curve (I-AUROC) and 7.3 points of area under the per-region overlap curve (AUPRO), raise PaDiM by 1.1 and 1.6 points, and leave CFLOW essentially unchanged. Ablations attribute the gains to the dual-task objectives and the shared projector, and evaluation-time routing with PatchCore shows that the image adapter serves image scores while the pixel adapter serves localisation maps.