## 4 Experiment Results  
To evaluate our method, we performed experiments on a famous domain adaptation benchmark dataset, the Office-31 dataset [14]. This dataset contains three image domains: DSLR, Amazon, and Webcam, and each of them has 31 classes with corresponding class names. In Figure 3, we compare the information quantity of feature maps for training with vs. without attention in Amazon. We can clearly see that adding attention helped the classifier acquire much more information, which means we can obtain higher test accuracy after adding attention. In this experiment, we took one domain as the source domain and another as the target domain. We defaulted to the labels of all source domain data being given, and the labels of all target data were unknown. Because there are three domains in the Office-31 dataset, we can conduct our experiment on six experiment settings, namely: \(A\to W\): (A)mazonas the source domain and (W)ebcam as the target domain; \(A\to D\): (A)mazon as the source domain and (D)SLRas the target domain; \(W\to A\): (W)ebcam as the source domain and (A)mazon as the target domain; \(W\to D\): (W)ebcam as the source domain and (D)SLR as the target domain; \(D\to A\): (D)SLR as the source domain and (A)mazon as the target domain; and \(D\to W\): (D)SLR as the source domain and (W)ebcam as the target domain. For comparative analysis of our method (P-ADC), in addition to Deep CORAL, we tested other well-known algorithms (deep domain confusion and conditional domain adversarial networks) on the Office-31 benchmark dataset. Deep Domain Confusion (DDC) [15] adds an adaptation layer and domain confusion loss in AlexNet. Conditional Domain Adversarial Networks (CDANs) [16] introduce multilinear conditioning and entropy conditioning to improve the discriminability and guarantee the transferability. Following [8], we initialized the weight of the last fully connected layer (fc8) with \(\mathcal{N}(0.0,0.005)\) and set the dimension to 31, the number of categories. The other layers of AlexNet were initialized with the pre-trained model parameters of ImageNet [17], keeping the layerwise parameter settings. We also set batch size = 128, learning rate = \(10^{-3}\), weight decay = \(5\times 10^{-4}\), and momentum = 0.9 for all of the experiments below (Table 1) for a fair comparison. From Table 1, we can see that P-ADC achieved higher average performance than Deep CORAL and the other baseline methods. In three out of six shifts, P-ADC\({}_{(2-3)}\) achieved the highest accuracy (\(l_{TOTAL}=L_{\text{C}}+\sum_{p=2}^{3}\sum_{i=1}^{t}\lambda_{i}l_{p-norm}\), where \(t\) is the number of p-norm loss layers in a deep network and P-ADC\({}_{(2-3)}\) means that \(p\) ranges from two to three). For the other three shifts, P-ADC\({}_{(2-4)}\) (\(l_{TOTAL}=L_{\text{C}}+\sum_{p=2}^{4}\sum_{i=1}^{t}\lambda_{i}l_{p-norm}\), where P-ADC\({}_{(2-4)}\) indicates that \(p\) ranges from two to four) obtained the best scores. In this experiment, we only tried P-ADC\({}_{(2-3)}\) and P-ADC\({}_{(2-4)}\) because the p-norm loss would take up many computing resources with the increase of \(p\), resulting in the computing speed declining dramatically. In addition, as we can see from Table 1, the test accuracy could not achieve the result of the official algorithm for all due to the fine-tuned AlexNet model from PyTorch, as well as the software and hardware environment. Figure 3: Comparison of feature maps with vs. without attention in Amazon. The 1 st column is the original image (sum of 3 channels); the 2 nd column is with attention (0 channels); the 3 rd column is with attention (sum of 256 channels); the 4 th column is without attention (sum of 256 channels); the 5 th column is an attention matrix. We can see the amount of information of the 4 th column is very small, just as the single channel (i.e., 0 channels) with attention in the 2 nd column. Figure 4 shows us three plots generated for shift \(D\to W\) to assist us in analyzing P-ADC. In Figure 4a, we visualize the process of training and testing on Deep CORAL and P-ADC.