KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems

• URL: http://arxiv.org/abs/2605.31596v1
• Date: Fri, 29 May 2026 17:58:01 GMT
• Title: KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems
• Abstract summary: We propose an OOD detection metric based on the Kullback-Leibler divergence between the diffusion prior and the posterior distribution.<n>We show that this metric can detect subtle yet semantically meaningful distribution shifts, such as the shift from healthy liver CT scans to those with tumors.
• Score: 5.090475631856756
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models have shown promising performance as data-driven priors for computational imaging, as well as some capacity to detect out-of-distribution (OOD) images. However, existing approaches to OOD detection often require some knowledge of the shifted distribution, fail to detect subtle or localized distribution shifts, and operate on full images, rather than the indirect measurements available in inverse problems. We propose an OOD detection metric based on the Kullback-Leibler divergence between the diffusion prior and the posterior distribution, that (i) does not require any calibration data or knowledge of the shifted distribution, and (ii) can detect whole images as OOD as well as localize OOD patches within an image. Experimentally, we show that this metric can detect subtle yet semantically meaningful distribution shifts, such as the shift from healthy liver CT scans to those with tumors, and generalizes across different types of diffusion models, datasets, and inverse problems. Our code can be found at https://github.com/voilalab/KLIP.

Related papers

• Reconstruction-Shift Discrimination via Mask-Guided Latent Diffusion for Medical Anomaly Detection [58.92871907037662]
  Unsupervised medical anomaly detection learns normal anatomical patterns from healthy training images and identifies deviations at test time.<n>Reconstruction-based and diffusion-based methods commonly use the difference between an input image and its reconstruction as anomaly evidence.<n>We propose discriminative mask-guided diffusion (DMD), a medical anomaly detection framework that complements residual-based localization with reconstruction-shift discrimination.
  arXiv  Detail & Related papers  (2026-08-01T04:56:39Z)
• Unsupervised Detection of Distribution Shift in Inverse Problems using Diffusion Models [15.926651846862544]
  We propose a fully unsupervised metric for estimating distribution shifts using only indirect (corrupted) measurements.<n>We show that our score-based metric, using only corrupted measurements, closely approximates the KL divergence computed from clean images.<n>Motivated by this result, we show that aligning the out-of-distribution score with the in-distribution score -- using only corrupted measurements -- reduces the KL divergence and leads to improved reconstruction quality across multiple inverse problems.
  arXiv  Detail & Related papers  (2025-05-16T17:44:58Z)
• Beyond Perceptual Distances: Rethinking Disparity Assessment for Out-of-Distribution Detection with Diffusion Models [28.96695036746856]
  Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection. DM-based methods bring fresh insights to the field, yet remain under-explored. Our work has demonstrated state-of-the-art detection performances among DM-based methods in extensive experiments.
  arXiv  Detail & Related papers  (2024-09-16T08:50:47Z)
• GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection [60.78684630040313]
  Diffusion models tend to reconstruct normal counterparts of test images with certain noises added. From the global perspective, the difficulty of reconstructing images with different anomalies is uneven. We propose a global and local adaptive diffusion model (abbreviated to GLAD) for unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2024-06-11T17:27:23Z)
• Out-of-Distribution Detection with a Single Unconditional Diffusion Model [54.15132801131365]
  Out-of-distribution (OOD) detection is a critical task in machine learning that seeks to identify abnormal samples. Traditionally, unsupervised methods utilize a deep generative model for OOD detection. This paper explores whether a single model can perform OOD detection across diverse tasks.
  arXiv  Detail & Related papers  (2024-05-20T08:54:03Z)
• Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models [72.07462371883501]
  We propose emphProjection Regret (PR), an efficient novelty detection method that mitigates the bias of non-semantic information. PR computes the perceptual distance between the test image and its diffusion-based projection to detect abnormality. Extensive experiments demonstrate that PR outperforms the prior art of generative-model-based novelty detection methods by a significant margin.
  arXiv  Detail & Related papers  (2023-12-05T09:44:47Z)
• AnoDODE: Anomaly Detection with Diffusion ODE [0.0]
  Anomaly detection is the process of identifying atypical data samples that significantly deviate from the majority of the dataset. We propose a new anomaly detection method based on diffusion ODEs by estimating the density of features extracted from medical images. Our proposed method not only identifie anomalies but also provides interpretability at both the image and pixel levels.
  arXiv  Detail & Related papers  (2023-10-10T08:44:47Z)
• DIRE for Diffusion-Generated Image Detection [128.95822613047298]
  We propose a novel representation called DIffusion Reconstruction Error (DIRE) DIRE measures the error between an input image and its reconstruction counterpart by a pre-trained diffusion model. It provides a hint that DIRE can serve as a bridge to distinguish generated and real images.
  arXiv  Detail & Related papers  (2023-03-16T13:15:03Z)
• Implicit field learning for unsupervised anomaly detection in medical images [0.8122270502556374]
  An auto-decoder feed-forward neural network learns the distribution of healthy images in the form of a mapping between spatial coordinates and probabilities over a proxy for tissue types. Anomalies are localized using the voxel-wise probability predicted by our model for the restored image. We tested our approach in the task of unsupervised localization of gliomas on brain MR images and compared it to several other VAE-based anomaly detection methods.
  arXiv  Detail & Related papers  (2021-06-09T16:57:22Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Why Normalizing Flows Fail to Detect Out-of-Distribution Data [51.552870594221865]
  Normalizing flows fail to distinguish between in- and out-of-distribution data. We demonstrate that flows learn local pixel correlations and generic image-to-latent-space transformations. We show that by modifying the architecture of flow coupling layers we can bias the flow towards learning the semantic structure of the target data.
  arXiv  Detail & Related papers  (2020-06-15T17:00:01Z)
• Unsupervised Lesion Detection via Image Restoration with a Normative Prior [6.495883501989547]
  We propose a probabilistic model that uses a network-based prior as the normative distribution and detect lesions pixel-wise using MAP estimation. Experiments with gliomas and stroke lesions in brain MRI show that the proposed approach outperforms the state-of-the-art unsupervised methods by a substantial margin.
  arXiv  Detail & Related papers  (2020-04-30T18:03:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.