AI RESEARCH
HIERAMP: Coarse-to-Fine Autoregressive Amplification for Generative Dataset Distillation
arXiv CS.CV
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ArXi:2603.06932v1 Announce Type: new Dataset distillation often prioritizes global semantic proximity when creating small surrogate datasets for original large-scale ones. However, object semantics are inherently hierarchical. For example, the position and appearance of a bird's eyes are constrained by the outline of its head. Global proximity alone fails to capture how object-relevant structures at different levels recognition. In this work, we investigate the contributions of hierarchical semantics to effective distilled data.