A Diffusion-Based Framework for Multi-Class Anomaly Detection
Authors: Haoyang He, Jiangning Zhang, Hongxu Chen, Xuhai Chen, Zhishan Li, Xu Chen, Yabiao Wang, Chengjie Wang, Lei Xie
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on MVTec-AD and Vis A datasets demonstrate the effectiveness of our approach which surpasses the stateof-the-art methods |
| Researcher Affiliation | Collaboration | 1College of Control Science and Engineering, Zhejiang University 2Youtu Lab, Tencent |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states "Code is available at https: //lewandofskee.github.io/projects/diad." However, this is a project page and not a direct link to a code repository like GitHub, GitLab, or Bitbucket, as per the strict definition provided. |
| Open Datasets | Yes | MVTec-AD (Bergmann et al. 2019) dataset simulates real-world industrial production scenarios, filling the gap in unsupervised anomaly detection. It consists of 5 types of textures and 10 types of objects, in 5,354 highresolution images from different domains. The training set contains 3,629 images with only anomaly-free samples. Vis A (Zou et al. 2022) dataset consists of a total of 10,821 high-resolution images, including 9,621 normal images and 1,200 anomaly images with 78 types of anomalies. |
| Dataset Splits | No | The paper mentions a training set and a test set, but does not explicitly describe a validation set or its split. |
| Hardware Specification | Yes | We train for 1000 epochs on a single NVIDIA Tesla V100 32GB with a batch size of 12. |
| Software Dependencies | No | The paper mentions using "Adam optimiser" and a "Gaussian filter" but does not specify version numbers for any software libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | We train for 1000 epochs on a single NVIDIA Tesla V100 32GB with a batch size of 12. Adam optimiser (Loshchilov and Hutter 2019) with a learning rate of 1e 5 is set. A Gaussian filter with σ = 5 is used to smooth the anomaly localization score. For anomaly detection, the anomaly score of the image is the maximum value of the averagely pooled anomaly localization score which undergoes 8 rounds of global average pooling operations with a size of 8 8. During inference, the initial denoising timestep T is set from 1,000. We use DDIM (Song, Meng, and Ermon 2021) as the sampler with 10 steps by default. |