Projection Regret: Reducing Background Bias for Novelty Detection via
  Diffusion Models
Sungik Choi Hankook Lee Honglak Lee Moontae Lee
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.