Industrial image anomaly detection (AD) is a critical issue that has been investigated in different research areas. Many works have attempted to detect anomalies by simulating anomalous samples. However, how to simulate abnormal samples remains a significant challenge. In this study, a method for simulating anomalous samples is designed. First, for the object category, patch extraction and patch paste are designed to ensure that the extracted image patches come from the objects and are pasted to the objects in the image. Second, based on the statistical analysis of various anomalies’ presence, a combination of data augmentation is proposed to cover various anomalies as much as possible. The method is evaluated on MVTec AD and BTAD datasets; the experimental results demonstrate that our method achieves an overall detection AUC of 97.6% in MVTec AD datasets, outperforming the baseline by 1.5%, and the improvement over VT-ADL method is 4.3% on the BTAD datasets, demonstrating our method’s effectiveness and generalization.

Industrial defect detection plays an important role in smart manufacturing and is widely used in various scenarios such as smart inspection and product quality control. Currently, although utilizing a framework for knowledge distillation to identify industrial defects has achieved great progress, it is still a significant challenge task to extract better image features and prevent overfitting for student networks. In this study, a reverse knowledge distillation framework with two teachers is designed. First, for the teacher network, two teachers with different architectures are used to extract the diverse features of the images from multiple models. Second, considering the different contributions of channels and different teacher networks, the attention mechanism and iterative attention feature fusion idea are introduced. Finally, to prevent overfitting, the student network is designed with a network architecture that is inconsistent with the teacher network. Extensive experiments were conducted on Mvtec and BTAD datasets, which are industrial defect detection datasets. On the Mvtec dataset, the average accuracy values of image-level and pixel-level ROC achieved 99.43% and 97.87%, respectively. On the BTAD dataset, the average accuracy values of image-level and pixel-level ROC reached 94% and 98%, respectively. The performance on both datasets is significantly improved, demonstrating the effectiveness of our method.

Efficient exploration remains a key challenge in cooperative multi-agent reinforcement learning (MARL), where the novelty of a joint situation may arise not only from unfamiliar individual observations but also from previously underrepresented interaction patterns among agents. Existing intrinsic-reward approaches commonly characterize novelty through state, observation, value, or prediction representations, which may not explicitly capture such relational changes. To address this issue, we propose Relationship Graph Reconstruction Exploration (RGRE), an intrinsic exploration method that characterizes novelty from the perspective of inter-agent relationships. RGRE first constructs an attention-derived relationship representation from agents’ local observations and then employs a graph autoencoder to learn its latent relational structure. The reconstruction discrepancy is used as a proxy for relational novelty and incorporated into the environmental reward as an intrinsic exploration bonus. Experiments on the Multi-Agent Particle Environment (MPE) Spread task with 3, 6, and 10 agents and on six StarCraft Multi-Agent Challenge (SMAC) scenarios show that RGRE achieves higher mean final performance than QMIX on all nine evaluated tasks. Based on the final-performance results, the average improvements are 5.82 percentage points in landmark occupation rate across the three MPE settings and 7.84 percentage points in test win rate across the six SMAC scenarios.