## 1 Introduction  
Neural networks have inherent inductive biases because of their learning algorithms, datasets, and architectural designs. These biases significantly influence the information that the network prioritizes while learning patterns from data and making predictions. In image processing, various model architectures, such as vision transformers and convolutional neural networks (CNNs), have been proposed and have demonstrated outstanding performance [1; 2; 3; 4; 5; 6]. Among these architectures, CNNs have been reported to exhibit texture bias, which is an inductive bias inherent to these networks [7; 8; 9]. Owing to this, CNNs are particularly sensitive to local features and textures in images, often preferring their interpretation to the objects' overall shape. A primary reason for texture bias in CNNs is the convolutional layer structure. These layers apply filters to the local regions of an image and extract features, such as edges and textures [10; 11; 12]. This process is repeated over the entire image, and the resulting feature maps are propagated to subsequent layers. This convolution operation makes CNNs highly sensitive to local image details, which are considered to contribute to texture bias, which is closely related to the model performance in image-classification tasks. CNNs can perform highly accurate classifications on datasets rich in texture information, such as natural images. However, performance can decrease in tasks where shapes and contours are crucial factors, such as classifying abstract images like sketches and cartoons. Therefore, designing and training the model with an appropriate bias for specific tasks is crucial for maximizing its recognition accuracy. Moreover, these biases significantly affect the performance of pre-trained CNN models for transfer learning in specific domains [13]. In domains wherein texture features are crucial, models with a texture bias can learn more efficiently. Conversely, models with a shape bias are expected to be more advantageous in domains where the shape is more important. Recently, considerable advances have been made in understanding and adjusting CNN biases. For example, [PERSON] et al. [9] revealed the differences in biases between CNNs and the human visual system and explored their impact on recognition robustness. Subsequent studies [14] discovered that models trained on large-scale datasets, such as vision transformers, exhibit less texture bias than CNNs. To address this texture bias, various approaches have been proposed, including methods that utilize multiple models [15; 16; 17] and image preprocessing techniques [18; 19]. Furthermore, [PERSON] et al. [20] proposed learning methods to eliminate shape/texture biases, which led to improved adversarial robustness. Our previous work [13] introduced a method for amplifying shape bias and mitigating texture bias in CNNs by modifying the ImageNet dataset using simple image separation techniques, which resulted in improved accuracy for specific domain datasets. This study builds on these findings by focusing on CNNs' texture and shape biases and their responses to adjustments in the kernel size and layers of the network. Modifications to the kernel size and dilation can be easily implemented. The results indicate that appropriate tuning of these elements can improve the recognition performance of CNNs for specific datasets. Furthermore, we propose a new method for quantitatively assessing shape and texture biases, and demonstrate that it can be applied to a broader range of datasets than traditional methods. Aligning the data biases with those of the model enables effective and efficient learning during transfer learning. The contributions of this paper can be summarized as follows:  
* It quantitatively shows that increasing the kernel size and dilation step improves the shape bias. Additionally, it demonstrates that altering the kernel sizes and dilation step can significantly enhance the performance of CNNs for shape-dominant domains, such as sketches and cartoons.