Previous unsupervised anomaly detection (UAD) methods often struggle with significant intra-class diversity; i.e., a class in a dataset contains multiple subclasses, which we categorize as Feature-Rich Anomaly Detection Datasets (FRADs). This is evident in applications such as unified setting and unmanned supermarket scenarios. To address this challenge, we developed MiniMaxAD: a lightweight autoencoder designed to efficiently compress and memorize extensive information from normal images. Our model utilizes a large kernel convolutional network equipped with a Global Response Normalization (GRN) unit and employs a multi-scale feature reconstruction strategy. The GRN unit significantly increases the upper limit of the network's capacity, while the large kernel convolution facilitates the extraction of highly abstract patterns, leading to compact normal feature modeling. Additionally, we introduce an Adaptive Contraction Loss (ADCLoss), tailored to FRADs to overcome the limitations of global cosine distance loss. MiniMaxAD was comprehensively tested across six challenging UAD benchmarks, achieving state-of-the-art results in four and highly competitive outcomes in the remaining two. Notably, our model achieved a detection AUROC of up to 97.0\% in ViSA under the unified setting. Moreover, it not only achieved state-of-the-art performance in unmanned supermarket tasks but also exhibited an inference speed 37 times faster than the previous best method, demonstrating its effectiveness in complex UAD tasks.

MiniMaxAD是一种轻量级自编码器，其采用大卷积核、全局响应归一化单元和多尺度特征重建策略，能够高效压缩和记忆正常图像的大量信息，克服了之前的无监督异常检测方法在具有明显内部类别多样性的数据集中的困难，并在多个基准测试中取得了最先进的结果。

MiniMaxAD：用于特征丰富的轻量级自编码器的异常检测