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..
SoftPatch: Unsupervised Anomaly Detection with Noisy Data
Authors: Xi Jiang, Jianlin Liu, Jinbao Wang, Qiang Nie, Kai WU, Yong Liu, Chengjie Wang, Feng Zheng
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments in various noise scenes demonstrate that Soft Patch outperforms the state-of-the-art AD methods on the MVTec AD and BTAD benchmarks and is comparable to those methods under the setting without noise. |
| Researcher Affiliation | Collaboration | 1Southern University of Science and Technology, Department of Computer Science and Engineering 2Tencent, Youtu Lab |
| Pseudocode | No | The paper includes diagrams (e.g., Figure 1, Figure 2) to illustrate the method but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code can be found in https://github.com/TencentYoutuResearch/Anomaly Detection-Soft Patch. |
| Open Datasets | Yes | Datasets. Our experiments are mainly conducted on the MVTec AD and BTAD benchmarks[11; 12]. MVTec AD contains 15 categories with 3629 training images and 1725 test images in total |
| Dataset Splits | Yes | MVTec AD contains 15 categories with 3629 training images and 1725 test images in total |
| Hardware Specification | Yes | All our experiments are run on Nvidia V100 GPU and repeated three times to report the average results. |
| Software Dependencies | No | The paper mentions using Wide-Res Net50 as backbone and other methods like Patch Core, Pa Dim, CFLOW, but does not specify software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | In the absence of specific instructions, the backbone of the feature extractor is Wide-Res Net50, and the coreset sampling ratio of Patch Core and Soft Patch is 10%. For MVTec AD images, we only use 256 x 256 resolution and center crops them into 224 x 224 along with a normalization. For BTAD, we use 512 x 512 resolution. We train a separate model for each class. ... we set the threshold τ in Soft Patch and the LOF-K to constant 0.15 and 6 for all noisy scenarios and classes. |