There have been significant advancements in anomaly detection in an
unsupervised manner, where only normal images are available for training.
Several recent methods aim to detect anomalies based on a memory, comparing the
input and the directly stored normal features (or trained features with normal
images). However, such memory-based approaches operate on a discrete feature
space implemented by the nearest neighbor or attention mechanism, suffering
from poor generalization or an identity shortcut issue outputting the same as
input, respectively. Furthermore, the majority of existing methods are designed
to detect single-class anomalies, resulting in unsatisfactory performance when
presented with multiple classes of objects. To tackle all of the above
challenges, we propose GRAD, a novel anomaly detection method for representing
normal features within a "continuous" feature space, enabled by transforming
spatial features into coordinates and mapping them to continuous grids.
Furthermore, we carefully design the grids tailored for anomaly detection,
representing both local and global normal features and fusing them effectively.
Our extensive experiments demonstrate that GRAD successfully generalizes the
normal features and mitigates the identity shortcut, furthermore, GRAD
effectively handles diverse classes in a single model thanks to the
high-granularity global representation. In an evaluation using the MVTec AD
dataset, GRAD significantly outperforms the previous state-of-the-art method by
reducing 65.0\% of the error for multi-class unified anomaly detection. The
project page is available at this https URL

本研究提出了一种新的异常检测方法 GRAD，通过将空间特征转换为坐标并映射到连续网格，成功地在 “连续” 特征空间中表示正常特征，并通过融合局部和全局正常特征来有效地处理不同类别的对象，实验证明 GRAD 在多类统一异常检测方面降低了 65.0% 的误差。