Unsupervised Out-of-Distribution Detection with Diffusion Inpainting
This addresses the problem of identifying out-of-domain data without labeled examples, which is incremental as it builds on existing diffusion model techniques.
The paper tackles unsupervised out-of-distribution detection by proposing the Lift, Map, Detect (LMD) method, which uses diffusion models to corrupt and map images, achieving competitive performance across various datasets.
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.