  Unsupervised out-of-distribution detection (OOD) seeks to identify
out-of-domain data by learning only from unlabeled in-domain data. We present a
novel approach for this task - Lift, Map, Detect (LMD) - that leverages recent
advancement in diffusion models. Diffusion models are one type of generative
models. At their core, they learn an iterative denoising process that gradually
maps a noisy image closer to their training manifolds. LMD leverages this
intuition for OOD detection. Specifically, LMD lifts an image off its original
manifold by corrupting it, and maps it towards the in-domain manifold with a
diffusion model. For an out-of-domain image, the mapped image would have a
large distance away from its original manifold, and LMD would identify it as
OOD accordingly. We show through extensive experiments that LMD achieves
competitive performance across a broad variety of datasets. Code can be found
at https://github.com/zhenzhel/lift_map_detect.
