To extend the proposed distillation method to larger and more diverse datasets beyond CIFAR-10, several strategies can be implemented:
Transfer Learning: Utilize transfer learning techniques to adapt the distillation method to larger datasets. Pretrained models on large datasets like ImageNet can be used as a starting point to extract DSVs and incorporate model knowledge into the distillation process.
Data Augmentation: Implement advanced data augmentation techniques to increase the diversity of the dataset. By augmenting the available data with transformations like rotation, scaling, and flipping, the synthesized dataset can capture a broader range of variations present in the larger dataset.
Ensemble Methods: Employ ensemble methods to combine multiple distilled datasets from different subsets of the larger dataset. By aggregating the distilled information from various parts of the dataset, the final distilled dataset can better represent the diversity of the entire dataset.
Hyperparameter Tuning: Optimize the hyperparameters of the distillation method to suit the characteristics of the larger dataset. This includes adjusting the weighting factors for the DKKT loss and DM loss, as well as fine-tuning the learning rates and optimization strategies for better performance.
By incorporating these strategies, the proposed distillation method can be effectively extended to handle larger and more diverse datasets, ensuring improved performance and generalization capabilities.