AI RESEARCH
DIVER:Diving Deeper into Distilled Data via Expressive Semantic Recovery
arXiv CS.CV
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ArXi:2605.12649v1 Announce Type: new Dataset distillation aims to synthesize a compact proxy dataset that is unreadable or non-raw from the original dataset for privacy protection and highly efficient learning. However, previous approaches typically adopt a single-stage distillation paradigm, which suffers from learning specific patterns that overfit on a prior architecture, consequently suppressing the expression of semantics and leading to performance degradation across heterogeneous architectures.