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Not All Instances Are Equally Valuable: Towards Influence-Weighted Dataset Distillation

arXiv — cs.LG•Monday, November 3, 2025 at 5:00:00 AM
PositiveArtificial Intelligence

Not All Instances Are Equally Valuable: Towards Influence-Weighted Dataset Distillation

A new study on dataset distillation highlights the importance of weighing the influence of different data instances. By recognizing that not all data points contribute equally, researchers aim to create more efficient synthetic subsets that maintain performance while cutting down on storage and computation costs. This approach could revolutionize how we handle large datasets, making it easier and more cost-effective for various applications in machine learning.
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