PRISM: Diversifying Dataset Distillation by Decoupling Architectural Priors

Brian B. Moser; Shalini Sarode; Federico Raue; Stanislav Frolov; Krzysztof Adamkiewicz; Arundhati Shanbhag; Joachim Folz; Tobias C. Nauen; Andreas Dengel

arXiv · 2511.09905

PRISM: Diversifying Dataset Distillation by Decoupling Architectural Priors

Brian B. Moser·Shalini Sarode·Federico Raue·Stanislav Frolov·Krzysztof Adamkiewicz·Arundhati Shanbhag·Joachim Folz·Tobias C. Nauen·Andreas Dengel

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Abstract

Dataset distillation (DD) promises compact yet faithful synthetic data, but existing approaches often inherit the inductive bias of a single teacher model. As dataset size increases, this bias drives generation toward overly smooth, homogeneous samples, reducing intra-class diversity and limiting generalization. We present PRISM (PRIors from diverse Source Models), a framework that disentangles architectural priors during synthesis. PRISM decouples the logit-matching and regularization objectives, supervising them with different teacher architectures: a primary model for logits and a stochastic subset for batch-normalization (BN) alignment. On ImageNet-1K, PRISM consistently and reproducibly outperforms single-teacher methods (e.g., SRe2L) and recent multi-teacher variants (e.g., G-VBSM) at low- and mid-IPC regimes. The generated data also show significantly richer intra-class diversity, as reflected by a notable drop in cosine similarity between features. We further analyze teacher selection strategies (pre- vs. intra-distillation) and introduce a scalable cross-class batch formation scheme for fast parallel synthesis. Code will be released after the review period.

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Brian B. Moser, Shalini Sarode, Federico Raue, Stanislav Frolov, Krzysztof Adamkiewicz, Arundhati Shanbhag, Joachim Folz, Tobias C. Nauen, Andreas Dengel. 2025-11-30. PRISM: Diversifying Dataset Distillation by Decoupling Architectural Priors. https://arxiv.org/abs/2511.09905

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