# Fast Fourier Asymmetric Context Aggregation Network:
## 4 Experiments
### Comparison with Existing Approaches
#### 4.5.1 Quantitative Comparison
Table 2 presents the quantitative comparison results on the VITON-HD dataset. Our proposed FCAN achieves the best performance across all evaluation metrics. Notably, the VITON-HD dataset contains approximately 40% less data compared to the SKFashion dataset, making the evaluation results more sensitive to the texture patch size. Under conditions of small texture patch sizes, only our proposed FCAN and ZITS maintain consistently high performance, while the performance of other baseline methods fluctuates significantly. Table 3 presents the quantitative comparison results on the Fashion-Gen dataset, showing that our method achieves the best performance across all metrics, further validating the effectiveness of our approach. Among all the baseline methods, MST [79] achieves performance metrics closest to FCAN under both standard and medium texture patch sizes. However, under small texture patch sizes, the performance of MST [79] deteriorates significantly, indicating that it lacks robustness to texture patch size and struggles to adapt to the varying requirements of different practical tasks. It is important to note that the clothing size ratio in Fashion-Gen is smaller compared to SKFashion and VITON-HD, and the dataset contains fewer clothing categories with simpler garment structures. As a result, under conditions with standard and medium-sized texture patches, the SSIM scores of all methods are relatively close. However, although the data scales of the VITON-HD and Fashion-Gen datasets are nearly half that of the SKFashion, our method still maintains robust performance on both datasets. This suggests that, in comparison to the baseline methods, our approach exhibits enhanced robustness to the scale of training samples, thereby ensuring the method's reliability. Table 4 presents a comparison of the model parameter counts for each method, showing that our approach has the smallest number of parameters. Combined with the results of the quantitative experiments, this demonstrates that our method is highly efficient in terms of parameter usage, achieving optimal performance with the fewest computational parameters. \begin{table}
\begin{tabular}{c l l l l l l l} \hline \hline
**Texture Patch Size** & & **FID\(\downarrow\)** & **PSNR\(\uparrow\)** & **LPIPS\(\downarrow\)** & **SSIM\(\uparrow\)** & **TOPIQ-FR\(\uparrow\)** & **TOPIQ-NR\(\uparrow\)** \\ \hline \multirow{8}{*}{Standard} & PAINT [56] & 16.93 & 25.15 & 0.08113 & 0.864 & 0.623 & 0.667 \\  & MST [79] & 10.99 & 28.24 & 0.04570 & 0.893 & 0.749 & 0.671 \\  & Albahar-MT [80] & 15.99 & 26.48 & 0.07617 & 0.870 & 0.570 & 0.635 \\  & EdgeConnect [81] & 11.51 & 28.03 & 0.04795 & 0.892 & 0.739 & 0.672 \\  & ZITS [61] & 10.36 & 27.58 & 0.04703 & 0.899 & **0.919** & 0.671 \\  & FCAN (Our) & **9.38** & **28.64** & **0.03886** & **0.907** & 0.917 & **0.681** \\ \hline \multirow{8}{*}{Medium} & PAINT [56] & 20.19 & 23.81 & 0.09090 & 0.830 & 0.587 & 0.665 \\  & MST [79] & 14.17 & 26.01 & 0.06412 & 0.844 & 0.721 & 0.663 \\  & Albahar-MT [80] & 18.48 & 24.28 & 0.10190 & 0.842 & 0.515 & 0.619 \\  & EdgeConnect [81] & 14.45 & 25.83 & 0.05732 & 0.870 & 0.678 & 0.667 \\  & ZITS [61] & 12.27 & 26.77 & 0.05401 & 0.886 & 0.901 & 0.664 \\  & FCAN (Our) & **11.19** & **27.94** & **0.04549** & **0.893** & **0.914** & **0.671** \\ \hline \multirow{8}{*}{Small} & PAINT [56] & 30.28 & 22.98 & 0.10920 & 0.806 & 0.502 & 0.643 \\  & MST [79] & 28.00 & 20.55 & 0.13210 & 0.782 & 0.459 & 0.630 \\ \cline{1-1}  & Albahar-MT [80] & 41.62 & 22.93 & 0.13670 & 0.809 & 0.447 & 0.595 \\ \cline{1-1}  & EdgeConnect [81] & 38.62 & 17.80 & 0.13970 & 0.778 & 0.455 & 0.628 \\ \cline{1-1}  & ZITS [61] & 15.41 & 25.76 & 0.06361 & 0.865 & 0.897 & 0.658 \\ \cline{1-1}  & FCAN (Our) & **14.32** & **26.92** & **0.05478** & **0.877** & **0.901** & **0.662** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative experimental results on the VITON-HD dataset.