Post-Distillation Hybrid Dataset Curation via Differentiated Valuation

Dataset distillation produces compact synthetic training sets, while coreset selection preserves real examples with natural instance-level variation. This complementarity motivates post-distillation Hybrid Dataset Curation (HDC), where a compact training subset is constructed from a fixed distilled set and real candidates. However, curating such hybrid subsets is challenging because both sources play different roles in training. Existing strategies either score both sources with the same agnostic criterion or determine the real-distilled composition before sample valuation, which limits adaptive hybrid subset construction. To address these limitations, we propose Differentiated Hybrid Curation (DHC), a framework for post-distillation HDC based on differentiated valuation. DHC first constructs a balanced and semantically aligned real-distilled candidate pool to make cross-source comparison more tractable. It then introduces the Differentiated Identity Score (DIS), which compares candidate responses under distilled and real experts to provide a sample-wise source-aware valuation signal. Finally, DHC combines DIS with gradient-based representativeness and diversity-aware updates to construct class-balanced hybrid subsets without prescribing a fixed real-distilled ratio. Experiments on ImageWoof, ImageNette, and ImageNet-1K show that DHC improves over strong coreset selection and dataset distillation baselines in most settings and remains competitive with hybrid curation baselines.