CIM directly aligns data distributions to condense large-scale datasets with minimal information loss, achieving new SOTA results on ImageNet-1K distillation at IPC=10.
Dataset distillation via the wasserstein metric.arXiv preprint arXiv:2311.18531
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DMGD achieves better performance than fine-tuned SOTA methods in dataset distillation on ImageNet subsets by using semantic matching through conditional likelihood optimization and OT-based distribution matching in a training-free diffusion setup.
Introduces a structural score on token composition in discrete visual token space that correlates with higher validation performance in distilled datasets and guides diffusion-based distillation.
DO-ALL applies dataset distillation to generate synthetic source anchors that stabilize continual test-time adaptation under evolving domains without storing original source data.
citing papers explorer
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Condensing Large-Scale Datasets Directly with Minimal Information Loss
CIM directly aligns data distributions to condense large-scale datasets with minimal information loss, achieving new SOTA results on ImageNet-1K distillation at IPC=10.
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DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models
DMGD achieves better performance than fine-tuned SOTA methods in dataset distillation on ImageNet subsets by using semantic matching through conditional likelihood optimization and OT-based distribution matching in a training-free diffusion setup.
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Structural Assessment for Understanding and Guiding Dataset Distillation in Discrete Token Space
Introduces a structural score on token composition in discrete visual token space that correlates with higher validation performance in distilled datasets and guides diffusion-based distillation.
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Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation
DO-ALL applies dataset distillation to generate synthetic source anchors that stabilize continual test-time adaptation under evolving domains without storing original source data.