## 2 Related Works
### Improving Shape Bias  
CNNs exhibit object-recognition capabilities similar to or better than those of humans; however, unlike humans, CNNs tend to recognize objects by primarily emphasizing local texture information rather than shape information[9]. [PERSON] et al. [14] constructed a dataset to test this bias and analyzed the texture bias. Subsequently, they evaluated the biases of humans and a wide range of vision models and proposed methods for aligning them in terms of shape and texture.  
[PERSON] et al. [18] noted that methods aimed at enhancing existing shape biases could result in dataset alternations, leading to deterioration in the accuracy of the original dataset. Therefore, they improved the shape bias without compromising the integrity of the dataset by separating and recombining the images into the foreground and background, which also improved the accuracy of the original dataset. [PERSON] et al. [19] discovered that training on both normal and blurred images could make CNNs robust against image blurring and slightly reduce their texture bias; however, they did not achieve fully robust object-recognition capabilities similar to those of humans. [PERSON] et al. [42] focused on biases and suggested that the root cause of texture bias lies within the data distribution. They confirmed that texture bias can be mitigated and out-of-distribution (OOD) performance can be improved by redesigning the label space. [PERSON] et al. [43] improved OOD performance and model robustness using frequency normalization for the kernel parameters. By contrast, generative models that have been used for classification have demonstrated biases resembling human shape bias, as well as human-level OOD performance.  
[PERSON] et al. [44] addressed the effective receptive field of vision transformers and suggested that enlarging the kernel size was more effective than deepening the model layers for expanding the receptive field. [PERSON] et al. [20] acknowledged that although shape and texture serve as complementary cues, datasets tend to be biased toward either shape or texture. They developed a straightforward algorithm that utilizes images with conflicting shape and texture information to augment the training data with the aim of eliminating both shape and texture biases.  
Some studies have also attempted to expand networks to mitigate shape bias. [PERSON] et al. [15] supplemented the conventional learning network with a separate network designed to extract edge features from input images, and employed distinct learning processes for edge and texture features. By simply introducing a new network, they enabled learning without altering the original network structure. Using these two networks improved the image classification and detection accuracy and increased the shape bias.  
[PERSON] et al. [16] divided images into shape, texture, and color components, and learned each component to organize and summarize their information to determine their respective contributions. [PERSON] et al. [17] further decomposed images into grayscale, edges, silhouettes, and texture, and extracted features in specific modalities and discussed explainability using biases. [PERSON] et al. [45] leveraged implicit prior knowledge within data for few-shot learning to learn more generalized features. This approach enhanced the generalization and robustness of the model, and lowered its vulnerability to texture biases and adversarial perturbations.  
Discussions regarding shape and texture biases in vision models often focus on differences between human perception and robustness against noise. Additionally, as shown in Table 1, methods to increase shape bias frequently involve data preprocessing or utilizing multiple networks. This study proposes evaluation metrics that leverage texture and shape biases to achieve efficient and effective transfer learning. Moreover, we focus on fundamental architectural components, such as kernel size and dilation rate, to enhance shape bias.