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Possibility for Proactive Anomaly Detection [26.157855481471334]
The purpose of time-series anomaly detection is to reduce potential damages or losses.<n>Existing anomaly detection models detect anomalies through the error between the model output and the ground truth (observed) value.<n>We present a ittextproactive approach for time-series anomaly detection based on a time-series forecasting model specialized for anomaly detection and a data-driven anomaly detection model.
arXiv Detail & Related papers (2025-04-15T21:25:02Z)
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)
Temporal Score Analysis for Understanding and Correcting Diffusion Artifacts [40.47880613758304]
Current solutions rely on supervised detectors, yet lack understanding of why these artifacts occur in the first place. We propose ASCED (Abnormal Score Correction for Enhancing Diffusion) that detects artifacts by monitoring abnormal score dynamics during the diffusion process. Unlike most existing methods that apply post hoc corrections, our mitigation strategy operates seamlessly within the existing diffusion process.
arXiv Detail & Related papers (2025-03-20T15:11:56Z)
Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning [0.0]
This paper investigates unsupervised anomaly detection in time-series data using reinforcement learning (RL) in the latent space of an autoencoder.<n>We use wavelet analysis to enhance anomaly detection, enabling time-series data decomposition into both time and frequency domains.<n>We calibrate the decision boundary by generating synthetic anomalies and embedding a supervised framework within the model.
arXiv Detail & Related papers (2025-02-05T15:02:40Z)
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)
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)
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)
Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection [90.32910087103744]
A few labeled anomaly examples are often available in many real-world applications. These anomaly examples provide valuable knowledge about the application-specific abnormality. Those anomalies seen during training often do not illustrate every possible class of anomaly. This paper tackles open-set supervised anomaly detection.
arXiv Detail & Related papers (2022-03-28T05:21:37Z)
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)

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