# Fast Fourier Asymmetric Context Aggregation Network:
## 4 Experiments
### Comparison with Existing Approaches
#### 4.5.3 Ablation Studies  
We conducted extensive ablation experiments to assess the importance of each component in our method and the performance impact it provides. Tables 5-7 present the results of the ablation experiments conducted on three datasets. Specifically, \"w/o Dual residual\" indicates the removal of the dual-path residual connections in FF-CDRB, \"w/o ACAM\" means the removal of ACAM, \"w/o Gating\" refers to the removal of the adaptive gating mechanism in ACAM, \"ResNet+ACAM\" denotes the use of standard ResNet residual blocks as a replacement for FF-CDRB in the content inference module of FCAN, and \"Skip Connection\" denotes replacing ACAM with conventional skip connection. Figure 14: Qualitative comparison results under standard texture patch sizes on the SKFashion dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Texture Patch Size** & **FID\(\downarrow\)** & **PSNR\(\uparrow\)** & **LPIPS\(\downarrow\)** & **SSIM\(\uparrow\)** & **TOPIQ-FR\(\uparrow\)** & **TOPIQ-NR\(\uparrow\)** \\ \hline \multirow{4}{*}{Standard} & w/o Dual residual & 10.07 & 28.66 & 0.04531 & 0.898 & 0.910 & 0.680 \\  & w/o ACAM & 11.48 & 27.84 & 0.04917 & 0.895 & 0.889 & 0.650 \\  & w/o Gating & 10.21 & 28.73 & 0.04238 & 0.902 & 0.907 & 0.671 \\  & ResNet+ACAM & 10.27 & 28.65 & 0.04311 & 0.901 & 0.914 & **0.684** \\  & Skip Connection & 10.40 & **28.74** & 0.04201 & 0.905 & 0.911 & 0.674 \\  & FCAN (Our) & **9.38** & **28.64** & **0.03886** & **0.907** & **0.917** & 0.681 \\ \hline \multirow{4}{*}{Medium} & w/o Dual residual & 11.71 & 27.81 & 0.04737 & 0.892 & 0.900 & 0.663 \\  & w/o ACAM & 14.15 & 26.74 & 0.05820 & 0.879 & 0.877 & 0.644 \\  & w/o Gating & 11.86 & 27.84 & 0.04836 & 0.891 & 0.902 & 0.659 \\  & ResNet+ACAM & 12.11 & 27.87 & 0.04821 & 0.884 & 0.910 & **0.672** \\  & Skip Connection & 14.09 & 26.78 & 0.05521 & 0.886 & 0.887 & 0.652 \\  & FCAN (Our) & **11.19** & **27.94** & **0.04549** & **0.893** & **0.914** & 0.671 \\ \hline \multirow{4}{*}{Small} & w/o Dual residual & 14.82 & 27.01 & 0.05679 & 0.864 & 0.891 & 0.651 \\  & w/o ACAM & 28.97 & 22.86 & 0.10223 & 0.830 & 0.834 & 0.613 \\  & w/o Gating & 15.36 & 27.16 & 0.05841 & 0.876 & 0.889 & 0.649 \\  & ResNet+ACAM & 14.76 & 27.58 & **0.05431** & **0.883** & **0.904** & 0.659 \\  & Skip Connection & 24.92 & 23.26 & 0.09333 & 0.848 & 0.840 & 0.602 \\  & FCAN (Our) & **14.32** & 26.92 & 0.05478 & 0.877 & 0.901 & **0.662** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation experiment results on the VITON-HD dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Texture Patch Size** & **FID\(\downarrow\)** & **PSNR\(\uparrow\)** & **LPIPS\(\downarrow\)** & **SSIM\(\uparrow\)** & **TOPIQ-FR\(\uparrow\)** & **TOPIQ-NR\(\uparrow\)** \\ \hline \multirow{4}{*}{Standard} & w/o Dual residual & 13.37 & 33.45 & 0.02205 & 0.961 & 0.784 & 0.521 \\  & w/o ACAM & 13.75 & 33.49 & 0.02291 & 0.960 & 0.758 & 0.494 \\  & w/o Gating & 13.27 & 33.05 & 0.02154 & 0.960 & 0.720 & 0.526 \\  & ResNet+ACAM & 13.14 & 33.18 & 0.02176 & 0.960 & 0.779 & **0.533** \\  & Skip Connection & 14.16 & **33.83** & 0.02268 & **0.964** & 0.754 & 0.501 \\  & FCAN (Our) & **12.73** & 33.41 & **0.020  
**FF-CDRB:** The ablation experiment results show that the proposed FF-CDRB effectively improves the FID, LPIPS, and TOPIQ metrics of the generated images, demonstrating its superiority in content inference. As mentioned in our qualitative comparison experiments, the clothing structures in the Fashion-Gen dataset are relatively simple, and the clothing regions occupy a smaller area, which is why the SSIM metric on the Fashion-Gen dataset is relatively similar. Table 8 presents a comparison of the model's computational parameter count after ablating each component. The results show that the FF-CDRB, compared to conventional convolution-based residual blocks, not only provides performance improvements but also reduces the model's computational parameters, achieving more efficient and lightweight content inference.