# Polyp Generalization via Diversifying Style at Feature-Level Space
## 1 Introduction
Style feature statistics of five datasets. Note that each feature represents concatenation of mean and standard deviation from earlier layers of EfficientNet for all datasets. styles. They proposed stylized versions of datasets using style transfer to enhance accuracy and generalizability. Similarly, image-level data augmentation using Generative Adversarial Networks (GANs) has been employed, but it cannot be applied universally across tasks [10]. In [13], Adaptive Instance Normalization (AdaIN) was employed to match the mean and variance of the content features with those of the style features. It takes both a content and style image as inputs, encoding them into the feature space at the encoder side. These encoded representations are then passed to an AdaIN layer, which adjusts the mean and variance of the content feature maps to match those of the style feature maps, thereby producing stylized feature maps. The final output is generated by a decoder from these stylized feature maps. In contrast, methods such as MixStyle [14] aim to boost style diversity by augmenting features directly at the feature level. This approach generates new styles by blending existing styles from seen source domains. However, existing style augmentation methods have limitations in fully representing real styles in unseen target domains, leading to reduced diversity in samples and potential performance decrements, particularly when significant differences exist between generated virtual styles and real unseen styles. In this study, we introduce a novel style-based data augmentation module operating at the feature-space level, tailored for the task of polyp generalization. Our approach aims to address challenges stemming from differences in style distribution between source and target images, which can solve generalization in the polyp domain, as shown in Figure 2. To tackle this, we extract style statistics, such as mean and standard deviation, from the early layers of a convolutional neural network (CNN). The basic assumption behind the style transfer between the polyp is that the network may have confirmation bias towards the style features (color and textural information) while learning. Transferring such features facilitates learning style-agnostic representations, which eventually improves the generalization problem. Our proposed method employs a style-aware encoder-decoder UNet network, integrating style information in the feature space. We utilize Adaptive Instance Normalization (AdaIN) to transfer or generate diverse style features, thereby reducing the style gap between training and unseen testing sources of polyp images while preserving original semantic features for segmentation. Through experiments, we demonstrate the effectiveness of transferring style features from unseen target images to source images during training, enhancing model generalizability. Additionally, increasing style diversity by mixing style features of two images improves model performance. We also introduce a novel approach to generating synthetic yet plausible styles to ensure minimal deviation in generated style features. Our primary objective is to mitigate domain shifts in polyp segmentation tasks while transferring knowledge from one source domain to multiple unseen domains. This scenario can be viewed as a single-domain generalization problem, wherein the segmentation model is trained on a single polyp dataset and applied to multiple unseen datasets. Extensive experiments conducted on five public polyp datasets validate the efficacy of our proposed method. Additionally, we evaluate our method by comparing it with the style augmentation technique conducted at the image level, as demonstrated in the study by [PERSON] et al. [15]. The contributions are listed below:  
* We introduce a novel style-aware UNet approach for the task of polyp generalization. This method enables the model to learn diverse style features from target style source images, thereby enhancing its generalization ability and effectiveness in unseen target sources.