GSDD: Generative Space Dataset Distillation for Image Super-resolution

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationTechnical Tracks 14
EditorsMichael Wooldridge, Jennifer Dy, Sriraam Natarajan
PublisherAssociation for the Advancement of Artificial Intelligence
Pages7069-7077
Number of pages9
Edition7
ISBN (Electronic)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
DOIs
StatePublished - 25 Mar 2024
Event38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, Canada
Duration: 20 Feb 202427 Feb 2024

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
Number7
Volume38
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

Conference38th AAAI Conference on Artificial Intelligence, AAAI 2024
Country/TerritoryCanada
CityVancouver
Period20/02/2427/02/24

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