## 4 Results and Discussion
### Ablation Study
#### 4.4.1 Component Ablation  
To guide the diffusion models to generate high-quality images on unlabeled datasets, we utilize three key methods: pseudo-label generation (PLG), pseudo-label matching (PLM), and label-matching refinement (LMR). So, we perform four gradually changing settings to validate the effectiveness of the three components: (1) We use unconditional diffusion models as a baseline for ablation experiments, as described in Section 4.2. (2) We utilize the pseudo-label generation (PLG) method to generate pseudo-labels for unlabeled datasets, and directly train and generate images on them using the guided diffusion model. (3) To generate images of specific real categories, we utilize the pseudo-label-matching (PLM) method to train and generate images. (4) Finally, we employ the label-matching  
Figure 2: Changes in sample quality under different iterations. Throughout the entire training process, GDUI outperforms the diffusion model trained with ground-truth labels in terms of FID, sFID, IS, and precision metrics in many cases, indicating that the superior performance of GDUI is not just a coincidence. Moreover, both GDUI and the diffusion model trained with ground-truth labels outperform the unconditioned diffusion model trained for 400K iterations, which achieved the best performance throughout the entire training process, in terms of FID, sFID, IS, and precision metrics. (**a**) FID at different iterations. (**b**) sFID at different iterations. (**c**) IS at different iterations. (**d**) Precision at different iterations. (**e**) Recall at different iterations.  
refinement (LMR) method to further improve the performance by adjusting irrelevant semantic information of the images.  
We show the results in Table 2. The initial performance of the baseline, i.e., the unconditional diffusion model, on all metrics except Recall is relatively low. Moreover, since it is built on an unlabeled dataset, it cannot generate images of specific real categories. By adopting the pseudo-label generation (PLG) method, the quality of the generated images improves in terms of FID, sFID, IS, and precision metrics. Particularly, there is a significant improvement in FID and precision metrics. However, the problem of generating images of specific real categories is not completely solved since only pseudo-labels are generated. The aforementioned problem is addressed by utilizing the pseudo-label-matching (PLM) method. Meanwhile, since the pseudo-label-matching (PLM) method only matches the clusters with pseudo-labels to the actual classes, without better aligning the images and labels, the quality of the generated images does not improve. Finally, we utilize the label-matching refinement (LMR) method to adjust the semantic information within the clusters with actual class information, in order to better align the images with the semantic information. It further improves the image quality while maintaining the ability to generate images of specific real categories.  
Figure 3: Qualitative comparisons on STL-10 dataset. Samples from our diffusion model GDUI (FID 16.66, **left**) compared to samples from a diffusion model based on ground-truth labels (FID 17.20, **middle**) and samples from a diffusion model based on an unlabeled dataset (FID 26.81, **right**).