  Recent studies highlighted a practical setting of unsupervised anomaly
detection (UAD) that builds a unified model for multi-class images, serving as
an alternative to the conventional one-class-one-model setup. Despite various
advancements addressing this challenging task, the detection performance under
the multi-class setting still lags far behind state-of-the-art class-separated
models. Our research aims to bridge this substantial performance gap. In this
paper, we introduce a minimalistic reconstruction-based anomaly detection
framework, namely Dinomaly, which leverages pure Transformer architectures
without relying on complex designs, additional modules, or specialized tricks.
Given this powerful framework consisted of only Attentions and MLPs, we found
four simple components that are essential to multi-class anomaly detection: (1)
Foundation Transformers that extracts universal and discriminative features,
(2) Noisy Bottleneck where pre-existing Dropouts do all the noise injection
tricks, (3) Linear Attention that naturally cannot focus, and (4) Loose
Reconstruction that does not force layer-to-layer and point-by-point
reconstruction. Extensive experiments are conducted across three popular
anomaly detection benchmarks including MVTec-AD, VisA, and the recently
released Real-IAD. Our proposed Dinomaly achieves impressive image AUROC of
99.6%, 98.7%, and 89.3% on the three datasets respectively, which is not only
superior to state-of-the-art multi-class UAD methods, but also surpasses the
most advanced class-separated UAD records.
