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 [1].
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
Authors: Haoyang He, Yuhu Bai, Jiangning Zhang, Qingdong He, Hongxu Chen, Zhenye Gan, Chengjie Wang, Xiangtai Li, Guanzhong Tian, Lei Xie
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on six diverse anomaly detection datasets and seven metrics demonstrate state-of-the-art performance, substantiating the method s effectiveness. |
| Researcher Affiliation | Collaboration | 1Zhejiang University 2Youtu Lab, Tencent 3Nanyang Technological University |
| Pseudocode | No | The paper describes the architecture and method components using textual descriptions and figures (Fig. 1, Fig. 2), but does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The code and models are available at https://lewandofskee.github.io/projects/Mamba AD. |
| Open Datasets | Yes | MVTec-AD [3] encompasses a diverse collection of 5 types of textures and 10 types of objects, 5,354 high-resolution images in total. 3,629 normal images are designated for training. The remaining 1,725 images are reserved for testing and include both normal and abnormal samples. Vis A [58] features 12 different objects... Real-IAD [42] includes objects from 30 distinct categories... More results on MVTec-3D [5], as well as newly proposed Uni-Medical [50, 2] and COCO-AD [52] datasets, can be viewed in Appendix 5. |
| Dataset Splits | No | 3,629 normal images are designated for training. The remaining 1,725 images are reserved for testing and include both normal and abnormal samples. (This only specifies train and test splits, no explicit validation split.) |
| Hardware Specification | Yes | The model undergoes a training period of 500 epochs for the multi-class setting, conducted on a single NVIDIA TESLA V100 32GB GPU. |
| Software Dependencies | No | The paper mentions 'Adam W optimizer' but does not specify specific version numbers for software dependencies or programming languages used. |
| Experiment Setup | Yes | All input images are resized to a uniform size of 256 256 without additional augmentation for consistency. A pre-trained Res Net34 acts as the feature extractor, while a Mamba decoder of equivalent depth [3,4,6,3] to Res Net34 serves as the student model for training. ... The Adam W optimizer is employed with a learning rate of 0.005 and a decay rate of 1 10 4. The model undergoes a training period of 500 epochs for the multi-class setting... |