Fast Adaptation to Super-Resolution Networks via Meta-Learning
- Title
- Fast Adaptation to Super-Resolution Networks via Meta-Learning
- Author
- 김태현
- Keywords
- Deep learning; Meta-learning; Single-image super-resolution; Patch recurrence
- Issue Date
- 2020-08
- Publisher
- springer
- Citation
- Computer Vision – ECCV 2020, page. 754-769
- Abstract
- Conventional supervised super-resolution (SR) approaches are trained with massive external SR datasets but fail to exploit desirable properties of the given test image. On the other hand, self-supervised SR approaches utilize the internal information within a test image but suffer from computational complexity in run-time. In this work, we observe the opportunity for further improvement of the performance of single-image super-resolution (SISR) without changing the architecture of conventional SR networks by practically exploiting additional information given from the input image. In the training stage, we train the network via meta-learning; thus, the network can quickly adapt to any input image at test time. Then, in the test stage, parameters of this meta-learned network are rapidly fine-tuned with only a few iterations by only using the given low-resolution image. The adaptation at the test time takes full advantage of patch-recurrence property observed in natural images. Our method effectively handles unknown SR kernels and can be applied to any existing model. We demonstrate that the proposed model-agnostic approach consistently improves the performance of conventional SR networks on various benchmark SR datasets.
- URI
- https://link.springer.com/chapter/10.1007/978-3-030-58583-9_45https://repository.hanyang.ac.kr/handle/20.500.11754/169916
- ISBN
- 978-3-030-58583-9
- DOI
- 10.1007/978-3-030-58583-9_45
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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