# Polyp Generalization via Diversifying Style at Feature-Level Space
## 4 Results
### Comparison with Image-Level Data Augmentation  
To compare our proposed approach with the traditional method of style-based data augmentation, we created a dataset of 30,000 images for a generalization task. Here, we transfer the style features from polyp images in other datasets onto the Kvasir-SEG dataset while keeping its original content features intact, inspired by the work of [PERSON] et al. [6].  
We applied a commonly used style transfer technique, AdaIN [13], following the methodology outlined by [PERSON] et al. [15], to generate stylized versions of the Kvasir-SEG dataset as shown in Figure 5. Each dataset in Kvasir-SEG was stylized using style features extracted from randomly selected images from CVC-ClinicDB, CVC-ColonDB, ETIS and KvasirCapsule. Additionally, we manually selected 50 target images from unseen datasets for training purposes. The selection of 50 target images was a deliberate choice aimed at achieving a balance between computational efficiency and maintaining style diversity within the dataset. A higher number of target images would impose a greater computational burden during the style transfer process, potentially hindering the practical feasibility of the approach. Conversely, a lower number of target images might compromise the diversity of styles represented in the dataset, limiting the model's ability to learn and generalize effectively. Therefore, the selection of 50 target images was aimed at striking a balance between these considerations, ensuring both computational feasibility and stylistic diversity. The rationale behind this selection was to transfer unique style characteristics from the target set and optimize the model accordingly. By curating a subset of images showcasing diverse style variations, we aimed to capture a representative sample of the style space present in the non-training set. This selective approach allows us to prioritize the most relevant style information for adaptation while mitigating potential computational burdens associated with incorporating the entire non-training dataset. No additional image transformation techniques, such as rotation, scaling or flipping, were applied. Furthermore, the image size remained consistent at 384 \(\times\) 384 pixels across all experimental settings.  
We present the quantitative results of the baseline model, traditional augmentation method and style augmentation in Table 1. The baseline Efficient UNet model trained on the stylized dataset achieved superior segmentation results across all evaluation metrics compared to the baseline model. This indicates that the use of style transfer as a data augmentation strategy significantly impacts the model's generalization performance. Furthermore, retraining the model on the original dataset led to marginal improvements in terms of Dice score and mIoU. Both the SCM and SGM achieved similar scores to those of style augmentation, with the distinction being that the SCM and SGM were applied in the feature space while style augmentation was performed at the image level. Lastly, our proposed method (the NSSM) demonstrated better performance compared to other methods, indicating its ability to learn more domain-irrelevant feature representations.