DiffusionAD: Denoising Diffusion for Anomaly Detection

Zhang, Hui; Wang, Zheng; Wu, Zuxuan; Jiang, Yu‐Gang

doi:10.48550/arxiv.2303.08730

Cited by 4 publications

(4 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous other methods enhance the model's ability to recover normal sample features by devising speci c masking strategies [26, 37,38] or synthetic pseudo-anomaly methods [39,40]. Additionally, some diffusion-based models [41,42] have demonstrated excellent performance, but due to the step-by-step reasoning over the image space, these models struggle to recover the features of normal samples. This method's performance is suboptimal in terms of memory occupation and inference speed.…”

Section: Reconstruction-based Methodsmentioning

confidence: 99%

“…We selected several SOTA IAD models as our baselines, including E cientAD-M [31], AST [44], S-T [43], and GCAD[6] based on S-T networks, FastFlow [32] based on normalizing ows, PatchCore [30] based on features, ComAD [27] based on pre-segmentation, DRAEM [39] and CutPaste [40] based on reconstruction, SLSG [24] based on graph self-attention mechanism, and DiffusionAD [42] based on a diffusion model. For FastFlow and PatchCore, we utilized WideResNet-50 as the backbone.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

See 1 more Smart Citation

GLAE: Global-local Feature Autoencoder for Image Logical Anomaly Detection

Peng,

Ye,

Zhuang

et al. 2024

Preprint

View full text Add to dashboard Cite

Self-supervised anomaly detection in industrial images holds significant practical value. While existing anomaly detection models excel in numerous public benchmarks, their substantial computational complexity and inability to detect logical anomalies hinder their widespread practical application. To address this issue, we proposes a global-local feature autoencoder (GLAE) for anomaly detection, which can be seamlessly integrated into the lightweight student-teacher (S-T) framework in parallel. GLAE uses a novel global feature extractor as the encoder (GFencoder), comprising solely of simple convolutions. This significantly reduces the computational cost while effectively capturing the global semantic information of the image, enabling the completion of global-local information reconstruction for the image. During the training stage, the student and GLAE were exclusively trained on normal samples, and they were unable to accurately capture the local or global features of abnormal samples during testing. By computing the feature distance between the teacher, student, and autoencoder, the local and global anomalies of the image were determined. We evaluated our method using three industrial anomaly detection dataset collections, and GLAE demonstrated state-of-the-art (SOTA) performance in image-level logical anomaly detection, efficiently handling anomalies with less than 7 ms latency on an NVIDIA RTX 3090 GPU. This establishes it as a cost-effective solution applicable in industrial scenarios, and it introduces a new approach for utilizing convolution to extract global features from images.

show abstract

Section: Reconstruction-based Methodsmentioning

confidence: 99%

Section: Comparison With Other Methodsmentioning

confidence: 99%

GLAE: Global-local Feature Autoencoder for Image Logical Anomaly Detection

Peng,

Ye,

Zhuang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…[20] proposed AnoDDPM, a model that leverages simplex noise and is the first to conceptualize partial diffusion strategies in the context of anomaly detection. [22] proposed a novel anomaly detection framework, Diffu-sionAD, which incorporates a denoising sub-network and a segmentation sub-network to achieve end-to-end anomaly detection and region localization. [23] presented a data augmentation method based on DDPM to enhance the detection performance for carbon fiber composite structures.…”

Section: Denoising Diffusion Probabilistic Modelsmentioning

confidence: 99%

Industrial Product Surface Defect Detection via the Fast Denoising Diffusion Implicit Model

wang,

Yang,

Liu

et al. 2023

Preprint

View full text Add to dashboard Cite

In the age of intelligent manufacturing, surface defect detection plays a pivotal role in the automated quality control of industrial products, constituting a fundamental aspect of smart factory evolution. Considering the diverse sizes and feature scales of surface defects on industrial products and the difficulty in procuring high-quality training samples, the achievement of real-time and high-quality surface defect detection through artificial intelligence technologies remains a formidable challenge. To address this, we introduce a defect detection approach grounded in the Fast Denoising Probabilistic Implicit Model. Firstly, we propose a noise predictor influenced by the spectral radius feature tensor of image. This enhancement augments the ability of generative model to capture nuanced details in non-defective areas, thus overcoming limitations in model versatility and detail portrayal. Furthermore, we present a loss function constraint based on the Perron root. This is designed to incorporate the constraint within the representational space, ensuring the denoising model consistently produces high-quality samples. Lastly, comprehensive experiments on both the Magnetic Tile and Market-PCB datasets, benchmarked against nine most representative models, underscore the exemplary detection efficacy of our proposed approach.

show abstract

“…AnoDDPM (Wyatt et al 2022) is the first approach to employ a diffusion model for medical anomaly detection. DiffusionAD (Zhang et al 2023a) utilizes an anomaly synthetic strategy to generate anomalous samples and labels, along with two sub-networks dedicated to the tasks of denoising and segmentation. DDAD (Mousakhan, Brox, and Tayyub 2023) employs a score-based pre-trained diffusion model to generate normal samples while finetuning the pre-trained feature extractor to achieve domain transfer.…”

Section: Introductionmentioning

confidence: 99%

A Diffusion-Based Framework for Multi-Class Anomaly Detection

He,

Zhang,

Chen

et al. 2024

AAAI

View full text Add to dashboard Cite

Reconstruction-based approaches have achieved remarkable outcomes in anomaly detection. The exceptional image reconstruction capabilities of recently popular diffusion models have sparked research efforts to utilize them for enhanced reconstruction of anomalous images. Nonetheless, these methods might face challenges related to the preservation of image categories and pixel-wise structural integrity in the more practical multi-class setting. To solve the above problems, we propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection, which consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion’s denoising network, and a feature-space pre-trained feature extractor. Firstly, The SG network is proposed for reconstructing anomalous regions while preserving the original image’s semantic information. Secondly, we introduce Spatial-aware Feature Fusion (SFF) block to maximize reconstruction accuracy when dealing with extensively reconstructed areas. Thirdly, the input and reconstructed images are processed by a pre-trained feature extractor to generate anomaly maps based on features extracted at different scales. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach which surpasses the state-of-the-art methods, e.g., achieving 96.8/52.6 and 97.2/99.0 (AUROC/AP) for localization and detection respectively on multi-class MVTec-AD dataset. Code will be available at https://lewandofskee.github.io/projects/diad.

show abstract

DiffusionAD: Denoising Diffusion for Anomaly Detection

Cited by 4 publications

References 48 publications

GLAE: Global-local Feature Autoencoder for Image Logical Anomaly Detection

GLAE: Global-local Feature Autoencoder for Image Logical Anomaly Detection

Industrial Product Surface Defect Detection via the Fast Denoising Diffusion Implicit Model

A Diffusion-Based Framework for Multi-Class Anomaly Detection

Contact Info

Product

Resources

About