Novelty detection is a fundamental task of machine learning which aims to
detect abnormal ($\textit{i.e.}$ out-of-distribution (OOD)) samples. Since
diffusion models have recently emerged as the de facto standard generative
framework with surprising generation results, novelty detection via diffusion
models has also gained much attention. Recent methods have mainly utilized the
reconstruction property of in-distribution samples. However, they often suffer
from detecting OOD samples that share similar background information to the
in-distribution data. Based on our observation that diffusion models can
\emph{project} any sample to an in-distribution sample with similar background
information, we propose \emph{Projection Regret (PR)}, an efficient novelty
detection method that mitigates the bias of non-semantic information. To be
specific, PR computes the perceptual distance between the test image and its
diffusion-based projection to detect abnormality. Since the perceptual distance
often fails to capture semantic changes when the background information is
dominant, we cancel out the background bias by comparing it against recursive
projections. Extensive experiments demonstrate that PR outperforms the prior
art of generative-model-based novelty detection methods by a significant
margin.

通过比较测试图像与其基于扩散模型的投影之间的感知距离以检测异常，Projection Regret (PR) 是一种有效的新颖性检测方法，通过与递归投影进行比较来取消背景偏差，实验证明 PR 在生成模型为基础的新颖性检测方法方面明显优于现有技术。