One potential limitation of the current approach is its reliance on clustering-based methods for pseudo-label generation, which may not always capture the underlying data distribution accurately, especially in scenarios with significant domain shifts or highly imbalanced datasets. To address this limitation, the algorithm could be further improved by incorporating domain adaptation techniques to align feature distributions between different domains. By integrating domain-invariant features into the learning process, the model can better generalize to unseen data from different domains. Additionally, techniques such as self-training or semi-supervised learning could be explored to leverage a small amount of labeled data to improve the model's performance in challenging scenarios. Furthermore, incorporating data augmentation strategies tailored to specific domain shifts or imbalances can help the model learn more robust and discriminative features.