SLFD wraps GLaD's latent-space dataset distillation in a training-time stochastic loss that samples classifier logits from a low-rank multivariate normal, reporting improved cross-architecture accuracy on several benchmarks.
Generalizing dataset distillation via deep generative prior
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Dataset Distillation with Probabilistic Latent Features
SLFD wraps GLaD's latent-space dataset distillation in a training-time stochastic loss that samples classifier logits from a low-rank multivariate normal, reporting improved cross-architecture accuracy on several benchmarks.