  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.
