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.

翻译：先前的无监督异常检测（UAD）方法在应对显著类内多样性时往往面临挑战，即数据集中某类包含多个子类，此类场景被我们归类为特征丰富异常检测数据集（FRAD）。这一问题在统一设置与无人超市等应用场景中尤为突出。为攻克该难题，我们开发了MiniMaxAD：一种轻量级自编码器，旨在高效压缩并记忆正常图像中的海量信息。该模型采用配备全局响应归一化（GRN）单元的大核卷积网络，并实施多尺度特征重构策略。GRN单元显著提升了网络容量上限，而大核卷积则有助于提取高度抽象的模式，从而实现紧凑的正常特征建模。此外，我们针对FRAD数据集引入自适应收缩损失（ADCLoss），以克服全局余弦距离损失的局限性。MiniMaxAD在六项具有挑战性的UAD基准测试中接受了全面评估，并在其中四项取得最优结果，在其余两项亦展现出强竞争力。值得注意的是，在统一设置下的ViSA任务中，我们的模型检测AUROC高达97.0%。更关键的是，该方法不仅在无人超市任务中达到最优性能，其推理速度较先前最优方法提升37倍，充分证实了其在复杂UAD任务中的有效性。