AI RESEARCH
EDITS: Enhancing Dataset Distillation with Implicit Textual Semantics
arXiv CS.CV
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ArXi:2509.13858v2 Announce Type: replace Dataset distillation aims to synthesize a compact dataset from the original large-scale one, enabling highly efficient learning while preserving competitive model performance. However, traditional techniques primarily capture low-level visual features, neglecting the high-level semantic and structural information inherent in images. In this paper, we propose EDITS, a novel framework that exploits the implicit textual semantics within the image data to achieve enhanced distillation.