LPQLD reduces soft label storage in dataset distillation by 78-500x on ImageNet datasets via pruning with dynamic reuse and quantization with student-teacher alignment, while improving accuracy.
Are large-scale soft labels necessary for large-scale dataset distillation? InThe Thirty-eighth Annual Conference on Neural Information Processing Systems
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Soft Label Pruning and Quantization for Large-Scale Dataset Distillation
LPQLD reduces soft label storage in dataset distillation by 78-500x on ImageNet datasets via pruning with dynamic reuse and quantization with student-teacher alignment, while improving accuracy.