Understanding Dataset Distillation via Spectral Filtering
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910050955558912 |
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| author | Bo, Deyu Liu, Songhua Wang, Xinchao |
| author_facet | Bo, Deyu Liu, Songhua Wang, Xinchao |
| contents | Dataset distillation (DD) has emerged as a promising approach to compress datasets and speed up model training. However, the underlying connections among various DD methods remain largely unexplored. In this paper, we introduce UniDD, a spectral filtering framework that unifies diverse DD objectives. UniDD interprets each DD objective as a specific filter function that affects the eigenvalues of the feature-feature correlation (FFC) matrix and modulates the frequency components of the feature-label correlation (FLC) matrix. In this way, UniDD reveals that the essence of DD fundamentally lies in matching frequency-specific features. Moreover, according to the filter behaviors, we classify existing methods into low-frequency matching and high-frequency matching, encoding global texture and local details, respectively. However, existing methods rely on fixed filter functions throughout distillation, which cannot capture the low- and high-frequency information simultaneously. To address this limitation, we further propose Curriculum Frequency Matching (CFM), which gradually adjusts the filter parameter to cover both low- and high-frequency information of the FFC and FLC matrices. Extensive experiments on small-scale datasets, such as CIFAR-10/100, and large-scale datasets, including ImageNet-1K, demonstrate the superior performance of CFM over existing baselines and validate the practicality of UniDD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01212 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Understanding Dataset Distillation via Spectral Filtering Bo, Deyu Liu, Songhua Wang, Xinchao Computer Vision and Pattern Recognition Machine Learning Dataset distillation (DD) has emerged as a promising approach to compress datasets and speed up model training. However, the underlying connections among various DD methods remain largely unexplored. In this paper, we introduce UniDD, a spectral filtering framework that unifies diverse DD objectives. UniDD interprets each DD objective as a specific filter function that affects the eigenvalues of the feature-feature correlation (FFC) matrix and modulates the frequency components of the feature-label correlation (FLC) matrix. In this way, UniDD reveals that the essence of DD fundamentally lies in matching frequency-specific features. Moreover, according to the filter behaviors, we classify existing methods into low-frequency matching and high-frequency matching, encoding global texture and local details, respectively. However, existing methods rely on fixed filter functions throughout distillation, which cannot capture the low- and high-frequency information simultaneously. To address this limitation, we further propose Curriculum Frequency Matching (CFM), which gradually adjusts the filter parameter to cover both low- and high-frequency information of the FFC and FLC matrices. Extensive experiments on small-scale datasets, such as CIFAR-10/100, and large-scale datasets, including ImageNet-1K, demonstrate the superior performance of CFM over existing baselines and validate the practicality of UniDD. |
| title | Understanding Dataset Distillation via Spectral Filtering |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2503.01212 |