## 4 Experiment
### Experimental Results
The first row is the pretrained model with ImageNet in the supervised learning setting. The models in the second and third rows are pretrained using the DINO framework (i.e., the SSL setting). \(+L_{\text{con}}\) is a pretrained model by applying our proposed consistency loss term. Figure 4: Visualization of masks created by thresholding the self-attention map at 70% of its mass. We demonstrate the resulting masks from fine-tuned models of ViT-S/16 trained with DINO and our method. Specifically, the first row shows the resulting mask for the model with only the last block fine-tuned using the DINO loss. The second row is the visualization result of the model trained with consistency loss under the same setting. our method encourages a model to better focus on the small patch regions to capture the subtle patterns. However, for the coarse-grained dataset, these patch regions are more likely to contain irrelevant background information compared with the fine-grained one. Hence, we conjecture that this negative effect might be caused by the use of background information as a shortcut in classification tasks. Next, the quality of the finer representation of ImageNet pretrained models was evaluated by using the \(k\)-NN classification accuracy on the FGVC datasets. In Table 3, we compare several SSL methods based on the ViT-S/16 architecture, including MoCov3 [20], iBoT [21], and DINO. The proposed method performed the best among all the SSL methods for the Aircraft, Car, and Flower datasets. Consistent with previous experimental results, the consistency loss was particularly effective on the Aircraft, Car, and Flower datasets. The proposed method demonstrated better performance in half of the fine-grained datasets, although it did not show the highest accuracy across all datasets. When we ranked these methods based on their performance on each dataset, the average rank of the proposed method was the best among the comparison targets. For a qualitative evaluation, we visualized the attention map of the pretrained model with ImageNet. We compared our proposed model with the primary baseline DINO, and the visualization results are shown in Figure 5. From these results, we can observe that our proposed model segments the target object more clearly than DINO. Overall, the model trained with the DINO framework tended to focus on small regions with high attention scores. In contrast, the model trained with consistency loss concentrates on larger areas of an object. These qualitative results demonstrate that the proposed consistency loss helps a model learn image-level representation by aggregating all informative local (i.e., patch-level) representations, not merely focusing on the most discriminative region. To further investigate the localization capability of the proposed method, we compared the semantic segmentation performance with DINO using the Flower and CUB datasets. Following the previous study [1], the segmentation results are obtained by thresholding the self-attention map from the ImageNet pretrained models, without further training for the segmentation task. As shown in Table 4, our proposed method clearly outperforms DINO on both datasets. These results suggest that the proposed consistency loss enhances the model's focus on object regions (i.e., foreground), revealing its potential applicability to dense prediction tasks, such as object detection and semantic segmentation. **Ablation study. ** Two hyperparameters are associated with the proposed consistency loss: the weight of the consistency loss \(\lambda\) and the output size of the RoI alignment layer. In this section, we describe an ablation study to further investigate the impact of each hyperparameter. For the RoI pooling size, we examine \(\{1\times 1,\ 3\times 3\}\) with default consistency loss weight (i.e., \(\lambda=0.5\)). For the weight of consistency loss, we consider three values of \(\{1,0.5,0.1\}\) with a default RoI pooling size of \(3\times 3\).