In the context of high usability in single-class anomaly detection models, recent academic research has become concerned about the more complex multi-class anomaly detection. Although several papers have designed unified models for this task, they often overlook the utility of class labels, a potent tool for mitigating inter-class interference. To address this issue, we introduce a Multi-class Implicit Neural representation Transformer for unified Anomaly Detection (MINT-AD), which leverages the fine-grained category information in the training stage. By learning the multi-class distributions, the model generates class-aware query embeddings for the transformer decoder, mitigating inter-class interference within the reconstruction model. Utilizing such an implicit neural representation network, MINT-AD can project category and position information into a feature embedding space, further supervised by classification and prior probability loss functions. Experimental results on multiple datasets demonstrate that MINT-AD outperforms existing unified training models.

利用多类别信息进行统一异常检测的多类隐式神经表示变换器(MINT-AD)可以在重构模型中减少类间干扰，通过学习多类别分布生成与类别相关的查询嵌入，并进一步通过分类和先验概率损失函数进行监督学习。实验结果表明，MINT-AD优于现有的统一训练模型。

朝多类异常检测迈进：对抗类别间干扰的类别感知统一模型探索