Temporal Score Analysis for Understanding and Correcting Diffusion Artifacts

• URL: http://arxiv.org/abs/2503.16218v1
• Date: Thu, 20 Mar 2025 15:11:56 GMT
• Title: Temporal Score Analysis for Understanding and Correcting Diffusion Artifacts
• Authors: Yu Cao, Zengqun Zhao, Ioannis Patras, Shaogang Gong,
• Abstract summary: Current solutions rely on supervised detectors, yet lack understanding of why these artifacts occur in the first place.<n>We propose ASCED (Abnormal Score Correction for Enhancing Diffusion) that detects artifacts by monitoring abnormal score dynamics during the diffusion process.<n>Unlike most existing methods that apply post hoc corrections, our mitigation strategy operates seamlessly within the existing diffusion process.
• Score: 40.47880613758304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual artifacts remain a persistent challenge in diffusion models, even with training on massive datasets. Current solutions primarily rely on supervised detectors, yet lack understanding of why these artifacts occur in the first place. In our analysis, we identify three distinct phases in the diffusion generative process: Profiling, Mutation, and Refinement. Artifacts typically emerge during the Mutation phase, where certain regions exhibit anomalous score dynamics over time, causing abrupt disruptions in the normal evolution pattern. This temporal nature explains why existing methods focusing only on spatial uncertainty of the final output fail at effective artifact localization. Based on these insights, we propose ASCED (Abnormal Score Correction for Enhancing Diffusion), that detects artifacts by monitoring abnormal score dynamics during the diffusion process, with a trajectory-aware on-the-fly mitigation strategy that appropriate generation of noise in the detected areas. Unlike most existing methods that apply post hoc corrections, \eg, by applying a noising-denoising scheme after generation, our mitigation strategy operates seamlessly within the existing diffusion process. Extensive experiments demonstrate that our proposed approach effectively reduces artifacts across diverse domains, matching or surpassing existing supervised methods without additional training.

Related papers

• Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection [15.572896213775438]
  This paper introduces a reformulation of the standard diffusion model geared toward selective region alteration. By modeling anomalies as noise in the latent space, our proposed textbfDeviation correction diffusion (Ours) model preserves the normal regions and encourages transformations on anomalous areas. Comprehensive evaluations demonstrate the superiority of our method in accurately identifying and localizing anomalies in complex images.
  arXiv  Detail & Related papers  (2025-03-25T05:14:40Z)
• Unsupervised Anomaly Detection Using Diffusion Trend Analysis [48.19821513256158]
  We propose a method to detect anomalies by analysis of reconstruction trend depending on the degree of degradation. The proposed method is validated on an open dataset for industrial anomaly detection.
  arXiv  Detail & Related papers  (2024-07-12T01:50:07Z)
• Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration [64.84134880709625]
  We show that it is possible to perform domain adaptation via the noise space using diffusion models. In particular, by leveraging the unique property of how auxiliary conditional inputs influence the multi-step denoising process, we derive a meaningful diffusion loss. We present crucial strategies such as channel-shuffling layer and residual-swapping contrastive learning in the diffusion model.
  arXiv  Detail & Related papers  (2024-06-26T17:40:30Z)
• Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection [2.209921757303168]
  Diffusion models have found valuable applications in anomaly detection by capturing the nominal data distribution and identifying anomalies via reconstruction. Despite their merits, they struggle to localize anomalies of varying scales, especially larger anomalies such as entire missing components. We present a novel framework that enhances the capability of diffusion models, by extending the previous introduced implicit conditioning approach Meng et al. 2022 in three significant ways.
  arXiv  Detail & Related papers  (2024-01-09T09:57:38Z)
• AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [59.08735812631131]
  Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. We propose AnomalyDiffusion, a novel diffusion-based few-shot anomaly generation model.
  arXiv  Detail & Related papers  (2023-12-10T05:13:40Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection [44.21198064126152]
  We propose a novel anomaly detection framework named ImDiffusion. ImDiffusion combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets.
  arXiv  Detail & Related papers  (2023-07-03T04:57:40Z)
• DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection [89.49600182243306]
  We reformulate the reconstruction process using a diffusion model into a noise-to-norm paradigm. We propose a rapid one-step denoising paradigm, significantly faster than the traditional iterative denoising in diffusion models. The segmentation sub-network predicts pixel-level anomaly scores using the input image and its anomaly-free restoration.
  arXiv  Detail & Related papers  (2023-03-15T16:14:06Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• Extreme Learning Machine for the Characterization of Anomalous Diffusion from Single Trajectories [0.0]
  I describe a simple approach to tackle the tasks of the AnDi challenge by combining extreme learning machine and feature engineering (AnDi-ELM) The method reaches satisfactory performance while offering a straightforward implementation and fast training time with limited computing resources.
  arXiv  Detail & Related papers  (2021-05-06T11:56:27Z)

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.