To further optimize the integration of model knowledge and data knowledge for enhanced performance in practical dataset distillation, the following strategies can be implemented:
Dynamic Weighting: Implement dynamic weighting schemes for the DKKT loss and DM loss based on the characteristics of the dataset. Adaptive weighting factors can be determined during training to prioritize either model knowledge or data knowledge based on the distillation progress.
Multi-Stage Distillation: Introduce a multi-stage distillation approach where model knowledge and data knowledge are integrated iteratively. In each stage, the distilled dataset is refined using feedback from both types of knowledge, leading to a more comprehensive and accurate representation of the dataset.
Regularization Techniques: Incorporate regularization techniques to prevent overfitting and enhance the generalization ability of the distilled dataset. Regularizers such as dropout, weight decay, or data augmentation can help balance the influence of model knowledge and data knowledge during distillation.
Ensemble Learning: Explore ensemble learning methods to combine the strengths of different distillation approaches. By integrating multiple distilled datasets generated using varied combinations of model knowledge and data knowledge, the final distilled dataset can benefit from diverse sources of information.
By optimizing the integration of model knowledge and data knowledge through these strategies, the distillation method can achieve even better performance in practical scenarios with limited dataset access, ensuring improved accuracy and robustness in dataset condensation.