DDEL: Closed-Loop Mutual Enhancement of Dataset Distillation and Ensemble Learning

arXiv:2611.00061v1 Announce Type: cross Abstract: Dataset distillation (DD) reduces a training set to a small set of examples, while ensemble learning (EL) combines predictions from several models. When applied in sequence, the distilled set remains fixed as the ensemble learns. We introduce DDEL, a framework that feeds ensemble behavior back into distillation. Each round trains the ensemble, measures loss and prediction disagreement on the source data, and uses these signals to guide the next data update. We implement this loop with distribution matching and a homogeneous three-member ensemble. Preliminary experiments cover skin lesion classification (ISIC 2019), treatment-response prediction (I-SPY1), and binary two-year prognosis classification (NSCLC-Radiomics). With the same images-per-class budget as the open-loop comparison, DDEL reaches balanced accuracy 0.3471 on ISIC and mean ROC-AUC 0.5120 and 0.5597 on I-SPY1 and NSCLC. The I-SPY1 difference is small, and the ISIC trajectory shows that more rounds do not consistently improve performance. These results offer initial evidence for studying DD-EL feedback, with its computational cost and robustness still requiring controlled evaluation.