While the UNPG approach offers significant benefits in improving the training of face recognition models, there are potential limitations that need to be considered. One limitation is the computational complexity associated with generating and filtering negative pairs, especially in large-scale datasets. As the dataset size increases, the process of selecting informative negative pairs and filtering out noisy pairs can become computationally intensive, potentially slowing down the training process. Additionally, the effectiveness of the noise-negative pair filtering technique may vary depending on the dataset characteristics and the specific task at hand. To address these limitations, further research could explore more efficient algorithms for negative pair generation and filtering, as well as investigate adaptive strategies that can dynamically adjust the selection criteria based on the dataset properties and model performance. Moreover, exploring the impact of different whisker sizes and filtering thresholds on model training could provide insights into optimizing the UNPG approach for diverse applications and datasets.