CLP-DD distills small synthetic datasets for linear probing on pre-trained models via closed-form inner solver and discriminative outer loss, matching or exceeding LGM+DSA performance at much lower cost on ImageNet-100 and ImageNet-1K.
Flexible dataset distillation: Learn labels instead of images
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C²R framework for robust dataset distillation prioritizes small-margin adversaries via a derived perturbation score and widens class boundaries with contrastive loss, yielding 2.8% average robust accuracy gains on CIFAR and ImageNet benchmarks.
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Closed-Form Linear-Probe Dataset Distillation for Pre-trained Vision Models
CLP-DD distills small synthetic datasets for linear probing on pre-trained models via closed-form inner solver and discriminative outer loss, matching or exceeding LGM+DSA performance at much lower cost on ImageNet-100 and ImageNet-1K.
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Mind Your Margin and Boundary: Are Your Distilled Datasets Truly Robust?
C²R framework for robust dataset distillation prioritizes small-margin adversaries via a derived perturbation score and widens class boundaries with contrastive loss, yielding 2.8% average robust accuracy gains on CIFAR and ImageNet benchmarks.