Boosting Dataset Distillation With the Assistance of Crucial Samples for Visual Learning
文献类型：期刊论文
| 作者 | Li, Xiaodan3,4; Zhu, Yao2; Chen, Yuefeng4; Chen, Cen3; Guo, Jianmei3; Wang, Shuhui1 |
| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
 |
| 出版日期 | 2025 |
| 卷号 | 27页码:9873-9886 |
| 关键词 | Training Semantics Computational modeling Visualization Synthetic data Manifolds Computational efficiency Training data Data models Continuing education Dataset distillation (DD) discarding semantic infinite interpolated |
| ISSN号 | 1520-9210 |
| DOI | 10.1109/TMM.2025.3618578 |
| 英文摘要 | In recent years, massive datasets have significantly driven the advancement of visual learning such as multi-modal large model at the expense of high computational costs and extensive storage requirements. Dataset distillation (DD) aims to address this challenge by learning a small synthetic dataset such that a model trained on it can achieve a test performance comparable to that of the model trained on the original dataset. This task can be formulated as a bi-level learning problem where the outer loop optimizes the learned dataset and the inner loop updates the model parameters based on the distilled data. Different from previous studies that focus primarily on optimizing the inner loop in this bi-level problem, we delve into the task of dataset distillation from the perspective of sample cruciality. We find that discarding easy samples and keeping the hard ones that are difficult to be represented by the learned synthetic samples in the outer loop can be beneficial for DD. Motivated by this observation, we further develop an Infinite Semantic Augmentation (ISA) based dataset distillation algorithm, which discards some easier samples and implicitly enriches harder ones in the semantic space through continuous interpolation between two target feature vectors. Through detailed mathematical derivation, the joint contribution to the training loss of all interpolated feature points is formed into an analytical closed-form solution of an integral that can be optimized with almost no extra computational cost. Experimental results on several benchmark datasets demonstrate the effectiveness of our approach in reducing the dataset size while preserving the accuracy of the model. Furthermore, we show that high-quality distilled data can also benefit downstream applications, such as continual learning and membership inference defense. |
| 资助项目 | National Natural Science Foundation of China[62202170] ; National Natural Science Foundation of China[62236008] |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:001641495700021 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42949]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Chen, Cen |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Beijing 100045, Peoples R China 2.Qiyuan Lab, Beijing 100850, Peoples R China 3.East China Normal Univ, Sch Data Sci & Engn, Shanghai 200050, Peoples R China 4.Alibaba Grp, Hangzhou 311121, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Xiaodan,Zhu, Yao,Chen, Yuefeng,et al. Boosting Dataset Distillation With the Assistance of Crucial Samples for Visual Learning[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2025,27:9873-9886. |
| APA | Li, Xiaodan,Zhu, Yao,Chen, Yuefeng,Chen, Cen,Guo, Jianmei,&Wang, Shuhui.(2025).Boosting Dataset Distillation With the Assistance of Crucial Samples for Visual Learning.IEEE TRANSACTIONS ON MULTIMEDIA,27,9873-9886. |
| MLA | Li, Xiaodan,et al."Boosting Dataset Distillation With the Assistance of Crucial Samples for Visual Learning".IEEE TRANSACTIONS ON MULTIMEDIA 27(2025):9873-9886. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。