## 5 Discussion and Limitations  
Most existing research has increased shape bias by modifying datasets or preparing multiple images. However, this study focuses on the fundamental structures of CNN architecture, such as kernel size and dilation, to enhance shape bias. Therefore, this approach can be applied to almost all models utilizing CNNs. By enlarging these sizes, we have effectively increased the receptive field of CNN models and ignored small, continuous  
Figure 3: Results of limiting the training data for each dataset. The accuracy rate is defined as the accuracy achieved with limited training data divided by the accuracy achieved with the entire dataset. The data ratio represents the proportion of the data used for training. (**a**) Results of reducing the amount of training data in the Logo dataset. (**b**) Results of reducing the amount of training data in the Cartoon dataset. (**c**) Results of reducing the amount of training data in the Sketch dataset.  
textures through dilation, thereby enhancing shape bias. Furthermore, by augmenting shape bias, we have improved the efficiency of transfer learning in datasets where shape information is paramount. Consequently, using models with texture bias for texture-rich natural images or fine-grained classification data, and models with shape bias for data where shape information is critical, such as sketch or cartoon images, can enhance accuracy and learning efficiency. Additionally, the proposed evaluation method enhances the efficiency of model transfer learning, making it a valuable approach for improving performance and learning with limited data. Moreover, this approach is compatible with any classification model since bias can be evaluated through short-term training of only the final layer. This method is particularly effective in high-precision scenarios and when training data is sparse, offering significant benefits in both cases.  
However, this study has several limitations. When varying the kernel size, the use of standard convolution operations typically increases the number of parameters. In such cases, the use of grouped convolution can mitigate the increase in parameters; however, this approach inevitably results in higher computational costs, thereby increasing the computation time. Furthermore, while there may be a desire to enhance the texture bias further, in models where the kernel size is three and the dilation rate is set to one, achieving any further enhancement to the texture bias becomes challenging. It is impractical to experiment with all datasets that are either shape-dominant or otherwise; thus, this research utilizes data with shape features. Additionally, as this study focuses on learning efficiency with limited data, the datasets used for evaluation are relatively small, and learning is conducted via linear probing, which only trains the classification layer. Hence, the effects of bias in training with large-scale datasets and the changes in bias through full fine-tuning remain unverified. Additionally, the newly proposed method for measuring shape and texture bias evaluates the biases present in vision models. To more accurately demonstrate the relationship between data and the model, techniques that measure data bias or assess both the data and the model simultaneously must be developed.