Balanced Dataset Distillation via Modeling Multiple Visual Pattern Distribution

Dataset Distillation (DD) aims to compress large-scale datasets into a small number of condensed Images Per Class (IPC), enabling efficient network training. Previous core-set selection and synthetic-based DD methods achieve reasonable performance. However, our in-depth investigation reveals that existing methods share a common issue: pattern imbalance. Specifically, they either overemphasize class-general patterns representing the majority of each class or focus on fewer marginal patterns critical for model generalization. To address this issue, we propose a novel framework, Balanced Patterns Selection (BPS). Unlike prior methods that assume each class forms a single cluster, BPS models the multiple visual pattern distribution within each class via a hierarchical semantic structure inherent to the dataset. It then selects two complementary subsets in a balanced manner from the center (class-general patterns) and the margins (marginal patterns) of each pattern, producing a pattern-balanced coreset. Theoretically, we prove that the BPS-selected coreset aligns with the original dataset in both distribution and performance. Moreover, its model-agnostic selection nature ensures cross-architecture generalization, while the Optimize-Once-for-All-IPCs property guarantees efficiency. Extensive experiments on four benchmarks demonstrate that BPS significantly outperforms existing state-of-the-art methods.