  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 challenge 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 employs a
technique that enhances feature diversity, thereby increasing the effective
capacity limit of the network. It also utilizes large kernel convolution to
extract highly abstract patterns, which contribute to efficient and compact
feature embedding. Moreover, we introduce an Adaptive Contraction Loss
(ADCLoss), specifically tailored to FRADs, to address the limitations of the
global cosine distance loss. In our methodology, any dataset can be unified
under the framework of feature-rich anomaly detection, in a way that the
benefits far outweigh the drawbacks. MiniMaxAD underwent comprehensive testing
across six challenging UAD benchmarks, achieving state-of-the-art results in
four and highly competitive outcomes in the remaining two. Notably, our model
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.
