Meganomaly: Extending Unsupervised Anomaly Detection to Hundreds of Categories

Recent multi-class unsupervised anomaly detection (MUAD) methods have nearly closed the gap with their single-class counterparts on small-scale benchmarks, yet suffer severe degradation when extended to hundreds of categories. This degradation is commonly attributed to over-generalization, where the reconstruction network learns to restore unseen anomalous patterns. In this work, we show that this explanation is incomplete. Through controlled scaling experiments, we identify __capacity saturation__ as a critical overlooked factor: as the number of categories grows, diverse normal patterns compete and interfere within the decoder's finite weights, causing normal reconstruction to saturate while anomaly reconstruction continues to improve. Guided by this insight, we present __Meganomaly__, a framework for large-scale MUAD based on a simple design principle: expanding decoder's capacity. Specifically, ShareMLP composes image-adaptive MLPs from a dynamic weight pool shared across decoder layers, substantially increasing capacity and reducing inter-category interference at little additional computational cost. To prevent anomalies from exploiting this expanded capacity, the Prior-Preserving Tight Bottleneck (PTB) aggressively compresses content information while preserving spatial and semantic priors that reconstruction relies on. Extensive experiments on seven datasets show that Meganomaly establishes state-of-the-art on large-scale benchmarks, improving I-AUROC by +5.5% on Real-IAD Variety (94.6%, 160 classes) and +7.1% on ADNet (89.1%, 380 classes).