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.
Dataset condensation with gradient matching
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.