Feb. 27, 2024, 5:44 a.m. | Hansong Zhang, Shikun Li, Pengju Wang, Dan Zeng, Shiming Ge

cs.LG updates on arXiv.org arxiv.org

arXiv:2312.15927v3 Announce Type: replace-cross
Abstract: Training state-of-the-art (SOTA) deep models often requires extensive data, resulting in substantial training and storage costs. To address these challenges, dataset condensation has been developed to learn a small synthetic set that preserves essential information from the original large-scale dataset. Nowadays, optimization-oriented methods have been the primary method in the field of dataset condensation for achieving SOTA results. However, the bi-level optimization process hinders the practical application of such methods to realistic and larger datasets. …

arxiv cs.cv cs.lg dataset mean type

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