# Deep Morphological Anomaly Detection Based on Angular Margin Loss
## 5 Experimental Results and Discussion
### Experimental Results on the MVTec Dataset  
MVTec contains 10 object and 5 texture categories for anomaly detection, with 3629 training instances and 1725 test samples [5]. The dataset is composed of \"normal\" images for training and both \"normal\" and \"abnormal\" images with various industrial defects for testing. In Table 1, we present the anomaly detection performance on the MVTec dataset. In comparison with the semantic-feature-based DAD model [7], the proposed algorithm yields a higher detection performance. This result demonstrates that extracting salient morphological features on \"normal\" images significantly increases the detection performance on industrial images. Compared to the results of our previous study [9], the results of this study show that self-supervised learning-based anomaly detection can be improved by merely applying AMLs. In addition, the proposed algorithm outperforms (02.6 AUROC) the existing DAD algorithm [32], which leverages pretrained networks (87.9 AUROC). The results on this dataset confirm that the combination of morphological transformations and AML-based self-supervised learning provides satisfactory performance in industrial anomaly detection problems. To illustrate the difference between the proposed algorithm and semantic feature-based DAD [7], a visual saliency map (visual representations) using Grad-CAM++ [34] is presented in Figure 4. Grad-CAM++ is a representative interpretable machine learning technique. This technology enables us to visually identify the parts of the input image that most critically influence the CNN's output. The MVTec dataset is divided into two overarching classes: \"texture\" and \"object.\" Figure 4 shows visualizations utilizing images of the \"carpet\" class within the texture class and the \"cable\" class in the object class. The first row of Figure 4 represents visualizations for the carpet class. For images corresponding to the texture class, the DAD model must detect all regions of the input image because they have morphological characteristics relevant to the entire image. Therefore, visualizations of the results by the proposed method (shown in the second and fifth columns in Figure 4) confirm that the saliency map appears in the input image when considered as a whole. In contrast, semantic feature-based DAD shows an unequal visual expansion with a high saliency score only for the edge region of the image. This is the primary reason that the proposed method has higher AUROC performance than existing methods in the texture class of the MVTec dataset. Unlike the texture class, subclasses belonging to the object class contain crucial object information in the center of the image. The second row in Figure 4 shows visual representations of the cable class. The proposed method, similar to the output in the carpet class, has a saliency map for the entire area of the image, but its saliency map is more intense in the vicinity of the object. Conversely, semantic feature-based DADs have high saliency map concentrations on wires and on the covering parts of cables. Most notably, based on the experiments using the cable class, in the \"abnormal\" image, the proposed method is quite accurate--it has a high saliency score in the area expressed as defective. This implies that the proposed method has sufficiently learned various morphological features of the image and proves that it is suitable for industrial anomaly detection problems.