Unsupervised Anomaly Detection Using Diffusion Trend Analysis
- URL: http://arxiv.org/abs/2407.09578v1
- Date: Fri, 12 Jul 2024 01:50:07 GMT
- Title: Unsupervised Anomaly Detection Using Diffusion Trend Analysis
- Authors: Eunwoo Kim, Un Yang, Cheol Lae Roh, Stefano Ermon,
- Abstract summary: 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.
- Score: 48.19821513256158
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Conventional anomaly detection techniques based on reconstruction via denoising diffusion model are widely used due to their ability to identify anomaly locations and shapes with high performance. However, there is a limitation in determining appropriate noise parameters that can degrade anomalies while preserving normal characteristics. Also, due to the volatility of the diffusion model, normal regions can fluctuate considerably during reconstruction, resulting in false detection. In this paper, we propose a method to detect anomalies by analysis of reconstruction trend depending on the degree of degradation, effectively solving the both problems of existing methods. The proposed method is validated on an open dataset for industrial anomaly detection, improving the performance of existing methods on a number of evaluation criteria. With the ease of combination with existing anomaly detection methods, it provides a tradeoff between computational cost and performance, allowing it high application potential in manufacturing industry.
Related papers
- Adversarially Robust Industrial Anomaly Detection Through Diffusion Model [23.97654469255749]
We propose a simple yet effective adversarially robust anomaly detection method, textitAdvRAD, that allows the diffusion model to act both as an anomaly detector and adversarial purifier.
Our proposed method exhibits outstanding (certified) adversarial robustness while also maintaining equally strong anomaly detection performance on par with the state-of-the-art methods on industrial anomaly detection benchmark datasets.
arXiv Detail & Related papers (2024-08-09T03:25:19Z) - Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with Dual Conditioning [30.4548093767138]
One-model-per-category methods often struggle with limited generalization capabilities.
Recent feature reconstruction methods, as representatives in one-model-all-categories schemes, face challenges including reconstructing anomalous samples and blurry reconstructions.
This paper creatively combines a diffusion model and a transformer for multi-class anomaly detection.
arXiv Detail & Related papers (2024-07-02T03:09:40Z) - 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) - An Iterative Method for Unsupervised Robust Anomaly Detection Under Data
Contamination [24.74938110451834]
Most deep anomaly detection models are based on learning normality from datasets.
In practice, the normality assumption is often violated due to the nature of real data distributions.
We propose a learning framework to reduce this gap and achieve better normality representation.
arXiv Detail & Related papers (2023-09-18T02:36:19Z) - Noise-to-Norm Reconstruction for Industrial Anomaly Detection and
Localization [5.101905755052051]
Anomaly detection has a wide range of applications and is especially important in industrial quality inspection.
Reconstruction-based methods use reconstruction errors to detect anomalies without considering positional differences between samples.
In this study, a reconstruction-based method using the noise-to-norm paradigm is proposed, which avoids the invariant reconstruction of anomalous regions.
arXiv Detail & Related papers (2023-07-06T08:06:48Z) - 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) - The role of noise in denoising models for anomaly detection in medical
images [62.0532151156057]
Pathological brain lesions exhibit diverse appearance in brain images.
Unsupervised anomaly detection approaches have been proposed using only normal data for training.
We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
arXiv Detail & Related papers (2023-01-19T21:39:38Z) - 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) - DR{\AE}M -- A discriminatively trained reconstruction embedding for
surface anomaly detection [14.234783431842542]
We propose a discriminatively trained reconstruction anomaly embedding model (DRAEM)
DRAEM learns a joint representation of an anomalous image and its anomaly-free reconstruction, while simultaneously learning a decision boundary between normal and anomalous examples.
On the challenging MVTec anomaly detection dataset, DRAEM outperforms the current state-of-the-art unsupervised methods by a large margin.
arXiv Detail & Related papers (2021-08-17T13:17:29Z)
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.