GSDD: Generative Space Dataset Distillation for Image Super-resolution

Haiyu Zhang, Shaolin Su, Yu Zhu, Jinqiu Sun, Yanning Zhang

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Single image super-resolution (SISR), especially in the real world, usually builds a large amount of LR-HR image pairs to learn representations that contain rich textural and structural information. However, relying on massive data for model training not only reduces training efficiency, but also causes heavy data storage burdens. In this paper, we attempt a pioneering study on dataset distillation (DD) for SISR problems to explore how data could be slimmed and compressed for the task. Unlike previous coreset selection methods which select a few typical examples directly from the original data, we remove the limitation that the selected data cannot be further edited, and propose to synthesize and optimize samples to preserve more task-useful representations. Concretely, by utilizing pre-trained GANs as a suitable approximation of realistic data distribution, we propose GSDD, which distills data in a latent generative space based on GAN-inversion techniques. By optimizing them to match with the practical data distribution in an informative feature space, the distilled data could then be synthesized. Experimental results demonstrate that when trained with our distilled data, GSDD can achieve comparable performance to the state-of-the-art (SOTA) SISR algorithms, while a nearly ×8 increase in training efficiency and a saving of almost 93.2% data storage space can be realized. Further experiments on challenging real-world data also demonstrate the promising generalization ability of GSDD.

源语言英语
主期刊名Technical Tracks 14
编辑Michael Wooldridge, Jennifer Dy, Sriraam Natarajan
出版商Association for the Advancement of Artificial Intelligence
7069-7077
页数9
版本7
ISBN(电子版)1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879
DOI
出版状态已出版 - 25 3月 2024
活动38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, 加拿大
期限: 20 2月 202427 2月 2024

出版系列

姓名Proceedings of the AAAI Conference on Artificial Intelligence
编号7
38
ISSN(印刷版)2159-5399
ISSN(电子版)2374-3468

会议

会议38th AAAI Conference on Artificial Intelligence, AAAI 2024
国家/地区加拿大
Vancouver
时期20/02/2427/02/24

指纹

探究 'GSDD: Generative Space Dataset Distillation for Image Super-resolution' 的科研主题。它们共同构成独一无二的指纹。

引用此