# Masked Style Transfer for Source-Coherent Image-to-Image Translation
## 4 Experimental Results
### Discussion  
Depending on the dataset, different styles are transferred. For CelebA-HQ, _male2 female_ and _female2 male_ were chosen. For the AFHQ dataset, we transferred _cat-dog2 wildlife_ and _wildlife2 cat-dog_. As can be seen from Figure 4, the proposed architecture can perform I2I translation between multiple domains similar to StarGANv2, but it gains the capability to preserve the intrinsic characteristics of the input during translation. Looking at the CelebA-HQ results, the proposed architecture maintains the input facial attributes but applies changes to the gender and hair color, which are taken from the reference images. In the AFHQ results, our method maintains the same expression and better preserves fur color during translation, but it changes the class of the animal. We introduced our work with the claim that it increases the diversity in the results generated using the same reference. This is shown clearly in Figures 5 and 6, where the results are compared with the ones obtained using StarGANv2. From these examples, it is evident that StarGANv2 tends to collapse to the reference image and loses the majority of the intrinsic attributes of the input except for the poses in AFHQ and the expressions in CelebA-HQ. In contrast, our results maintain many more of the original details, such as fur colors in AFHQ and ages and hair styles in CelebA-HQ. This leads to more diversity and variety in the generated images and to less reference-based generation. More specifically, StarGANv2 seems to capture attributes from the reference, like hair color and length in Figure 5, and apply them to the input in a rigid scheme that does not maintain the input attributes. On the contrary, the proposed method, in addition to understanding what the main details are from the reference, also considers the details from the input image before applying the transformation. This leads, for instance, to a higher variety of hair lengths rather than a prefixed hair length, like with StarGANv2. This is also visible in Figure 6, where StarGANv2 produces the same animal with different poses. Our method, on the contrary, better understands the input characteristics and generates different breeds of dog/cat/wildlife animals based on the input breed. Figure 4: Main results obtained with our architecture. We can see how the results are indistinguishable from real images and how they maintain the intrinsic characteristics of the input, like the expression, age, or fur color. Figure 5: Comparison between StarGANv2 (first rows) and our architecture (second rows) on CelebA-HQ dataset. Figure 6: Comparison between StarGANv2 (first rows) and our architecture (second rows) on AFHQ dataset. All the previous considerations are also valid when the style code \(s_{trg}\) is sampled from random noise by using the mapping network \(M\). This is presented in Figure 7, where our architecture produces various and more source-coherent results than the ones generated by StarGANv2. In order to support our claim, we also show how our method produces similar results when similar reference images are employed, as shown in Figure 8. This can be seen as a positive effect due to our source-coherent method that does not ignore input attributes. The above considerations are reflected in the quantitative results reported in Table 1. The proposed architecture significantly improves the LPIPS results for both datasets. Furthermore, the FID results highlight how our architecture produces much higher-quality images. As shown in Table 2, we also compute the FID using the StarGANv2 algorithm on CelebA-HQ, and we obtain opposite results. This is due to how the FID works (this is also explained in Section 4.4): given the alignment of the StarGANv2 generated images with the reference image shown in the qualitative results, the FID computed as in the StarGANv2 original paper is natively lower. Indeed, the FID tends to measure the difference between two