The selective relabel strategy, while effective in filtering out confusing mixed samples and encouraging the model to decrease its activation for non-salient features, may have limitations in handling more complex mixed samples. One potential limitation is the reliance on the teacher model's predictions to identify wrongly predicted samples, which may not always be accurate, especially in cases of highly ambiguous or challenging mixed samples. To address this limitation, the strategy could be further improved by incorporating ensemble methods or uncertainty estimation techniques to enhance the reliability of identifying confusing samples. Additionally, introducing a feedback mechanism that iteratively refines the relabeling process based on the model's performance during training could help improve the handling of complex mixed samples.