FD$^2$: A Dedicated Framework for Fine-Grained Dataset Distillation

1. 1Decoupling the dataset distillation pipeline into pretraining, sample distillation, and soft-label generation
2. 2Utilizing fine-grained class information to optimize sample distillation
3. 3Generating soft labels that capture detailed class relationships
4. 4Applying the FD$^2$ framework to various datasets and tasks to evaluate its effectiveness

Machine learning researchers and engineers on a team can benefit from FD$^2$ as it enables more efficient and effective dataset distillation, while data scientists can apply the framework to various applications

💡 FD$^2$ improves dataset distillation efficiency and accuracy by leveraging fine-grained class information

🚀 FD$^2$: A dedicated framework for fine-grained dataset distillation! 💡