## 4 Experiments
### Results  
The experimental results are listed in Table 4, and the Grad-CAM visualization of the proposed method is shown in Figure 2.  
For the shape-dominated dataset, the original model exhibited the lowest accuracy, except with a kernel size of five. Although the original ResNeXt model exhibited the highest accuracy for the ImageNet validation set, the results suggest that changing the kernel size or extension affects the shape/texture bias. The increase in the accuracy for the \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & **Proposed** & **Prior** & **Sketch** & **Cartoon** & **Logo** & **MPEG7** & **Animal2000** \\  & **Shape/Texture Bias** & **Shape Bias** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline Original & 0.4442 & 0.2000 & 32.06 & 50.75 & 31.46 & 58.52 & 54.80 \\ Kernel size: 5 & 0.4773 & 0.2896 & 30.41 & 47.38 & 31.28 & 52.78 & 54.90 \\ Kernel size: 7 & 0.4887 & 0.2959 & 35.18 & 54.53 & **34.80** & 64.91 & **63.60** \\ Dilation: 2 & 0.5036 & 0.2778 & 40.92 & 52.16 & 32.26 & 66.06 & 60.60 \\ Dilation: 3 & 0.5196 & 0.2935 & **41.96** & **57.74** & 33.85 & **67.21** & 61.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results to evaluate shape bias for each architectural change: accuracy on the shape-dominant dataset, two measures of shape bias, and the number of parameters. The shape-dominant dataset was trained five times and the average accuracy of those five times was taken as the final result. Higher values for each of the shape/texture bias indices indicate stronger shape bias. The same is true for the prior shape bias. The bold text in the table indicates the highest accuracy, while the underlined text represents the second-highest accuracy. shape-dominant dataset as the kernel size and dilation factor increased also suggests that shape bias increased as the architectural variations increased. Comparisons of the shape/texture bias metrics indicated that the indices from prior studies increased the shape bias compared to the original ResNeXt. However, the shape-dominant dataset accuracies were inconsistent. According to the conventional metric, the model with the highest shape bias had a kernel size of five, followed by that with a dilation factor of three. The accuracy for sketch images, which are strongly indicative of shape bias, was the highest for a dilation factor of three, followed by a dilation factor of two, which was inconsistent with the learning accuracy after transfer learning. However, the proposed shape/texture bias metric exhibited congruence between the post-transfer-learning accuracy and the evaluation metric across all datasets. This may be because the conventional shape-bias evaluation metric was developed to compare human visual processing with that of vision models. Consequently, the relationship between transfer learning and shape/texture bias was not prioritized. By contrast, the proposed metric was designed to specifically determine whether an image's shape or texture was preferred. Therefore, it was expected that the proposed method would be able to clearly elucidate the relationship between data and shape bias. Next, the results of the transfer learning for each dataset were analyzed. As mentioned previously, sketch images represented the most shape-dominant data, followed by cartoons and logo images. Models with a high shape bias owing to an altered dilation factor showed the most significant improvement in accuracy for sketch images, followed by cartoons and  
Figure 2: Results of Grad-CAM visualization used with the proposed shape/texture bias metric. In the case of the original ResNeXt (a texture-biased model), the heat map of the lower image (which is texture-dominant) turns red, indicating that the model is focusing on it. On the other hand, for the ResNeXt with a dilation rate of three (a shape-biased model), the heat map of the upper image (which is shape-dominant) turns red, indicating its focus.