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PRISM: Diversifying Dataset Distillation by Decoupling Architectural Priors

arXiv — cs.LG•Friday, November 14, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The introduction of PRISM highlights a significant advancement in dataset distillation, addressing the limitations of traditional single-teacher models that often yield homogeneous samples. This is particularly relevant in the context of related works like Facial-R1, which also focuses on enhancing data quality through explainable reasoning in facial emotion analysis. Additionally, the challenge of class imbalance in multi-view contexts, as discussed in Trusted Multi-view Learning, underscores the importance of diverse datasets for robust AI applications. PRISM's approach to decoupling architectural priors not only improves intra-class diversity but also sets a precedent for future research in dataset generation, emphasizing the need for innovative frameworks that can adapt to complex data environments.
— via World Pulse Now AI Editorial System
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