This paper presents FreD, a novel parameterization method for dataset
distillation, which utilizes the frequency domain to distill a small-sized
synthetic dataset from a large-sized original dataset. Unlike conventional
approaches that focus on the spatial domain, FreD employs frequency-based
transforms to optimize the frequency representations of each data instance. By
leveraging the concentration of spatial domain information on specific
frequency components, FreD intelligently selects a subset of frequency
dimensions for optimization, leading to a significant reduction in the required
budget for synthesizing an instance. Through the selection of frequency
dimensions based on the explained variance, FreD demonstrates both theoretical
and empirical evidence of its ability to operate efficiently within a limited
budget, while better preserving the information of the original dataset
compared to conventional parameterization methods. Furthermore, based on the
orthogonal compatibility of FreD with existing methods, we confirm that FreD
consistently improves the performances of existing distillation methods over
the evaluation scenarios with different benchmark datasets. We release the code
at this https URL

本文提出了一种新颖的参数化方法，利用频域对大型原始数据集进行数据集提炼，通过频谱变换优化数据实例的频率表示，以在有限的预算内操作并更好地保留原始数据集的信息。此外，通过与现有方法的正交兼容性，验证了该方法在不同基准数据集的评估场景中持续改善了现有提炼方法的性能。