HyperTrans: Efficient Hypergraph-Driven Cross-Domain Pattern Transfer in Image Anomaly Detection
Authors: Tengyu Zhang, Deyu Zeng, Baoqiang Li, Wei Wang, Wei Liu, Zongze Wu
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that Hyper Trans offers significant advantages in anomaly classification and anomaly segmentation tasks, outperforming state-of-the-art non-cross-domain methods in image-wise ROCAUC by 13%, 12%, and 15% in 1shot, 2-shot, and 5-shot settings on MVTec3D AD. |
| Researcher Affiliation | Academia | 1School of Software Engineering, Xi an Jiaotong University, 2Shenzhen University, 3Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), 4Wuhan University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Optimization algorithm for Hyper Trans |
| Open Source Code | Yes | The domain perturbation module is integrated into layers 3 to 10 of the backbone, employing Perlin noise with a mean of 0 and a standard deviation of 0.75 for domain perturbations.1 1https://github.com/ra Rn0y/Hyper Trans |
| Open Datasets | Yes | Dataset: The MVTec-3D AD dataset [Bergmann et al., 2021] is a standard 3D anomaly detection dataset, on which our experiments are performed. |
| Dataset Splits | Yes | It consists of 10 categories, a total of 2656 training samples, and 1137 testing samples. The 3D scans were acquired by a high-resolution industrial 3D sensor using structured light. Due to the scarcity of defective samples, only normal samples are contained in the training set of MVTec-3D AD, while the testing set contains both normal and defective samples. |
| Hardware Specification | Yes | We implement Hyper Trans using Py Torch2.4.0 and conduct evaluations on an NVIDIA A100. |
| Software Dependencies | Yes | We implement Hyper Trans using Py Torch2.4.0 and conduct evaluations on an NVIDIA A100. |
| Experiment Setup | Yes | Initial values of other parameters are listed in Table 2. Table 2: Parameter settings of Hyper Trans in our experiments. ̄ ̃ ̄ epochs P epochs R 0.3 0.6 3 100 |