DualAD: Exploring Coupled Dual-Branch Networks for Multi-Class Unsupervised Anomaly Detection
Shiwen He, Yuehan Chen, Liangpeng Wang, Wei Huang, Rong Xu, Yurong Qian · Electronics · 2025
Anomaly detection (AD) is crucial in various domains such as industrial inspection, medical diagnosis, and video surveillance. Previous advancements in unsupervised AD often necessitated training separate models for different objects, which can be inefficient when dealing with diverse categories in real-world scenarios. This paper addresses the recently proposed task of multi-class unsupervised anomaly detection (MUAD), which is more practical and challenging. We begin by reviewing the first MUAD framework, UniAD, and analyzing the characteristics of end-to-end feature reconstruction networks that can adapt to various backbone architectures. Building on these insights, we introduce a novel MUAD framework called DualAD. Our approach is based on the innovative design of a Coupled Dual-Branch Network (CDBN), which integrates a Wide–Shallow Network (WSN) with a Narrow–Deep Network (NDN), leveraging the strengths of both to achieve superior performance. We explore a fully transformer-based homogeneous design for the CDBN and introduce a more lightweight heterogeneous CDBN design that integrates a transformer with a Memory-Augmented Multi-Layer Perceptron (MMLP). Experimental results on the MVTec AD and VisA datasets demonstrate that DualAD outperforms the recent state-of-the-art methods and exhibits robust performance across various pre-trained backbone architectures.