Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection
Authors: Songmin Dai, Yifan Wu, Xiaoqiang Li, Xiangyang Xue
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation and demonstrate that GRAD achieves competitive anomaly detection accuracy and superior inference speed. |
| Researcher Affiliation | Academia | Songmin Dai1*, Yifan Wu1*, Xiaoqiang Li1 , Xiangyang Xue2 1School of Computer Engineering and Science, Shanghai University 2School of Computer Science, Fudan University laodar@shu.edu.cn, Victor Wu@shu.edu.cn, xqli@shu.edu.cn, xyxue@fudan.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | To validate the effectiveness and generalizability of our approach, we perform experiments on both MVTec AD (Bergmann et al. 2019) and MVTec LOCO (Bergmann et al. 2022). |
| Dataset Splits | Yes | Each sub-dataset in MVTec AD and MVTec LOCO contains limited training images. To train competitive detectors from scratch for each small sub-dataset, we adopt general data augmentations on both normal and generated images like previous works(Bergmann et al. 2019, 2022). |
| Hardware Specification | Yes | Anomaly detection performance vs. latency per image on an NVIDIA Tesla V100 GPU. |
| Software Dependencies | No | The paper does not provide specific software dependency versions (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | Patch-level Detector. Each sub-dataset in MVTec AD and MVTec LOCO contains limited training images. To train competitive detectors from scratch for each small sub-dataset, we adopt general data augmentations on both normal and generated images like previous works(Bergmann et al. 2019, 2022). For level-34, 68, and 136 detectors, the images are respectively resized into 256 256, 128 128, and 64 64. We train the detector on batches of size 128 (k + 2) for 2,000 epochs and report the accuracy of the final epoch. Each batch contains 128 randomly cropped positive patches from 4 normal images and 128 (k + 1) negative patches from 4 normal images and 4k contrastive images, where k equals the number of levels of used generated contrastive images. |