## 5 Discussion  
Both the enhanced models and the consensus models outperformed the raw models, irrespective of the network structures and dataset configurations. We can attribute this  
Figure 14: The inference results for six models, each based on different network structures, are presented along with their corresponding images and ground truth (GT) masks. The layout is as follows: (**a**) the image is shown at the top and the GT mask at the bottom; (**b**) results from the Simple Network; (**c**) results from the U-Net; and (**d**) results from DeepLabV3. The inference results for each network are organized in a \(2\times 3\) grid. The first row contains the results from single-dataset models, while the second row displays the results from multi-dataset models. In the column direction, the results are listed in the order of the raw model, enhanced model, and consensus model. improvement to the supplementation or modification of GT masks, as the redundant models help compensate for inconsistencies both within and across datasets. The proposed method offers not only improved performance but also computational efficiency. Traditional ensemble methods require multiple models to be used during runtime, which causes execution time to rise in direct proportion to the number of models involved. In contrast, the proposed method enhances performance without extending runtime, as it integrates redundancy within the generated training data, allowing for the training of a single, improved model (see Figure 15). We trained redundant models using the simple network structure as a baseline to verify the effectiveness of the method. Using heterogeneous network structures in practical applications can enhance the variability of redundant models, leading to more reliable collective intelligence. It is important to research how to select network structures, as performance discrepancies can lead to downward leveling. In the multi-dataset case, we expected all models to be improved by the additional guest datasets. But for the DeepLabV3, the raw FM showed a lower performance than the single-dataset raw model by 0.51%. The discrepancies between the two datasets appear to be the cause, and the major difference between DeepLabV3 and the other two network structures is the lack of a decoder structure, which could be the main factor. The consensus FM outperformed the raw FM by 2.76%, but it still fell short of the best single-dataset model, the enhanced model, by 0.18%. In the scenario involving multiple datasets, we distinguished between host and guest datasets; therefore, the guest dataset was expected to align with the host dataset. However, it is possible to generate redundant models for each dataset, which can aid in estimating the confidence levels of another dataset. The differences in class configurations between the two datasets used in this study were minor; thus, more complex relationships, such as inclusion relationships, should be taken into account. With these points in mind, our future research will focus on developing a more configurable and interactive multi-dataset fusion approach based on redundant models (see Figure 16). Finally, this method has the potential to be extended to various regression tasks, including denoising, pan-sharpening, super-resolution, and depth estimation. In regression, the model estimates continuous values instead of discrete class indices. Consequently, pixel-wise confidence levels may be based on the statistics of the estimated values themselves,  
Figure 16: Creating a unified model by integrating heterogeneous datasets by interactive fusion. Figure 15: Comparison of the training and inference phases: (**a**) conventional ensemble methods and (**b**) the proposed method. which can be derived from redundant models, rather than on the probability of accurate predictions. Similar to semantic segmentation, we could adjust the strength of parameter updates based on the uncertainty of the estimated values or correct the target values when the majority of models consistently predict distant values.