We present the efficiency of semi-orthogonal embedding for unsupervised anomaly segmentation. The multi-scale features from pre-trained CNNs are recently used for the localized Mahalanobis distances with significant performance. However, the increased feature size is problematic to scale up to the bigger CNNs, since it requires the batch-inverse of multi-dimensional covariance tensor. Here, we generalize an ad-hoc method, random feature selection, into semi-orthogonal embedding for robust approximation, cubically reducing the computational cost for the inverse of multi-dimensional covariance tensor. With the scrutiny of ablation studies, the proposed method achieves a new state-of-the-art with significant margins for the MVTec AD, KolektorSDD, KolektorSDD2, and mSTC datasets. The theoretical and empirical analyses offer insights and verification of our straightforward yet cost-effective approach.

翻译：我们展示了半垂直嵌入为不受监督的异常分化而建立半垂直嵌入的效率。 受过训练的有线电视新闻网的多尺度功能最近被用于局部的Mahalanobis距离上,且性能显著。 但是,增加的特征大小对于扩大至更大的有线电视有问题,因为它需要多维共变异度的分批反射。 在这里,我们把一种特别方法,随机特征选择, 推广为半垂直嵌入, 用于稳健近似, 分立式降低多维共变振的反向计算成本。 在对通货膨胀研究的仔细研究下, 提议的方法实现了一种新的艺术状态, 并有显著的利润, 用于MVTec AD、 KolektorSDDD、 KolektorSDD2 和 mSTC 数据集。 理论和经验分析为我们直截面但具有成本效益的方法提供了洞察和验证。