Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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 EMAIL; EMAIL; |
| 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. |