In addition to intra-class variation, other types of model capability metrics could be explored to guide the adaptive sample selection and outlier filtering process. One potential metric is the inter-class margin, which measures the separation between different classes in the feature space. By considering the inter-class margin, the algorithm can prioritize samples that contribute to maximizing class separability, leading to better feature representations. Another metric could be the uncertainty of the model predictions, where samples with higher uncertainty scores are given more weight during training to improve model confidence. Additionally, exploring metrics related to data density or class distribution could help in identifying samples that are more representative of the underlying data distribution, further enhancing the model's learning process.