arXiv:2603.25144v2 Announce Type: replace-cross Abstract: Dataset distillation (DD) compresses a large training set into a small synthetic set, reducing storage and training cost, and has shown strong results on general benchmarks. Decoupled DD further improves efficiency by splitting the pipeline into pretraining, sample distillation, and soft-label generation. However, existing decoupled methods largely rely on coarse class-label supervision and optimize samples within each class in a nearly identical manner. On fine-grained datasets, this often yields distilled samples that (i) retain large
The continuous growth in the scale and complexity of AI models and datasets necessitates more efficient training methods like dataset distillation to manage computational and storage burdens.
Improved dataset distillation, particularly for fine-grained datasets, directly reduces the computational and data storage overheads of AI development, making advanced models more accessible and faster to train.
The ability to distil fine-grained datasets more effectively means AI training can proceed with significantly smaller, yet equally representative, synthetic datasets, accelerating development cycles.
- · AI researchers and developers
- · Cloud computing providers
- · AI-driven industries
- · Edge AI applications
- · None
Reduced computational costs and time for training complex AI models, especially in highly specialized domains.
Faster iteration and deployment of AI systems, leading to quicker market adoption of new AI capabilities.
Democratization of advanced AI development as the barrier to entry for training sophisticated models is lowered for smaller entities and regions without massive compute resources.
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Read at arXiv cs.AI