A Unified Model for Multi-class Anomaly Detection
Authors: Zhiyuan You, Lei Cui, Yujun Shen, Kai Yang, Xin Lu, Yu Zheng, Xinyi Le
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm on MVTec-AD and CIFAR-10 datasets, where we surpass the state-of-the-art alternatives by a sufficiently large margin. |
| Researcher Affiliation | Collaboration | 1Shanghai Jiao Tong University 2Tsinghua University 3CUHK 4Sense Time |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. The method is described in text and via diagrams. |
| Open Source Code | Yes | Code is available at https:// github.com/zhiyuanyou/Uni AD. |
| Open Datasets | Yes | MVTec-AD [4] is a comprehensive, multi-object, multi-defect industrial anomaly detection dataset with 15 classes. CIFAR-10 [23] is a classical image classification dataset with 10 categories. |
| Dataset Splits | No | The paper describes how classes are used for training and testing (e.g., normal samples for training, anomalous for testing, unified/separate cases for MVTec-AD and CIFAR-10 class combinations), but does not explicitly provide percentages or counts for train/validation/test dataset splits, nor does it clearly define a separate validation set with specific size or methodology. |
| Hardware Specification | Yes | Our model is trained for 1000 epochs on 8 GPUs (NVIDIA Tesla V100 16GB) with batch size 64. |
| Software Dependencies | No | The paper states 'implemented in Python 3.8' but does not specify versions for other key software components or deep learning frameworks used (e.g., PyTorch, TensorFlow, or specific library versions). |
| Experiment Setup | Yes | The image size is selected as 224 224, and the size for resizing feature maps is set as 14 14. The reduced channel dimension is set as 256. Adam W optimizer [21] with weight decay 1 10 4 is used. Our model is trained for 1000 epochs on 8 GPUs (NVIDIA Tesla V100 16GB) with batch size 64. The learning rate is 1 10 4 initially, and dropped by 0.1 after 800 epochs. The layer numbers of the encoder and decoder are both 4. The neighbor size, jittering scale, and jittering probability are set as 7 7, 20, and 1, respectively. The evaluation is run with 5 random seeds. |