AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model
Authors: Teng Hu, Jiangning Zhang, Ran Yi, Yuzhen Du, Xu Chen, Liang Liu, Yabiao Wang, Chengjie Wang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our model significantly outperforms the state-of-the-art methods in generation authenticity and diversity, and effectively improves the performance of downstream anomaly inspection tasks. |
| Researcher Affiliation | Collaboration | Teng Hu1*, Jiangning Zhang2*, Ran Yi1 , Yuzhen Du1, Xu Chen2, Liang Liu2, Yabiao Wang2, Chengjie Wang1,2 1Shanghai Jiao Tong University 2Youtu Lab, Tencent {hu-teng, ranyi, Haaaaaaaaaa}@sjtu.edu.cn; {vtzhang, cxxuchen, leoneliu, caseywang, jasoncjwang}@tencent.com; |
| Pseudocode | No | No section or figure explicitly labeled 'Pseudocode' or 'Algorithm' was found. |
| Open Source Code | Yes | The code and data are available in https://github.com/sjtuplayer/anomalydiffusion. |
| Open Datasets | Yes | Dataset. we conduct experiments on the widely used MVTec (Bergmann et al. 2019) dataset. |
| Dataset Splits | No | The paper states, 'We employ one-third of the anomaly data with the lowest ID numbers as the training set, reserving the remaining two-thirds for testing.' A specific validation split is not explicitly mentioned. |
| Hardware Specification | No | No specific hardware details (like GPU models, CPU types, or cloud instance specifications) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper mentions software components like 'Latent Diffusion Model (LDM)', 'U-Net', 'Res Net-50', and 'Feature Pyramid Networks (FPN)' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Implementation details. We assign k = 8 tokens for anomaly embedding, n = 4 tokens for spatial embedding, and k = 4 tokens for mask embedding. |