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This paper introduces Influence Matching (Inf-Match), a dataset distillation method that aligns the final training outcome by learning a compact synthetic set whose effect on converged parameters matches that of the full dataset. It achieves state-of-the-art accuracy on classification benchmarks and outperforms strong baselines on vision-language distillation tasks.
This paper introduces TAKE (Trajectory-Aware Knowledge Estimation), a text dataset distillation framework that uses influence functions and optimal transport to reduce datasets to as little as 0.1% of their original size while preserving downstream task fidelity.
This paper proposes Rank-Aware Hyperbolic Alignment (RAHA), a method for vision-language dataset distillation that leverages hyperbolic geometry and alignment capacity control to efficiently compress large image-text datasets into high-quality synthetic pairs.
Google Devs discusses using dataset distillation to train smaller models on clean, structured examples to stabilize parser outputs and teach models repeatable behavior.
DO-ALL is a plug-and-play framework that uses dataset distillation to generate synthetic anchors that summarize source data, enabling stable long-term continual test-time adaptation without retaining original source data.
This paper introduces Domain Generalizable Dataset Distillation (DGDD), a new problem setting that targets out-of-distribution generalization of distilled datasets, and proposes Spectral Gradient Surgery (SGS) to disentangle class-discriminative and domain-specific information by leveraging cross-domain gradient agreement in the spectral domain.