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Gradient amplification for gradient matching based dataset distillation
DOI:10.1016/j.neunet.2025.107819.png)
Abstract
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• Label cycle shifting strategy produces informative gradient information. • The early exit mechanism alleviates matching the useless gradients. • Ensembling distilled datasets makes the training process more stable. • Gradient matching and distribution matching mutually enhance each other.
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