Fast Adaptation to Super-Resolution Networks via Meta-learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Seobin | - |
dc.contributor.author | Yoo, Jinsu | - |
dc.contributor.author | Cho, Donghyeon | - |
dc.contributor.author | Kim, Jiwon | - |
dc.contributor.author | Kim, Tae Hyun | - |
dc.date.accessioned | 2022-07-07T17:34:25Z | - |
dc.date.available | 2022-07-07T17:34:25Z | - |
dc.date.created | 2021-05-11 | - |
dc.date.issued | 2020-08 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145341 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
dc.title | Fast Adaptation to Super-Resolution Networks via Meta-learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Tae Hyun | - |
dc.identifier.doi | 10.1007/978-3-030-58583-9_45 | - |
dc.identifier.scopusid | 2-s2.0-85097374393 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.12372 LNCS, pp.754 - 769 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 12372 LNCS | - |
dc.citation.startPage | 754 | - |
dc.citation.endPage | 769 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Benchmarking | - |
dc.subject.keywordPlus | Computer vision | - |
dc.subject.keywordPlus | Optical resolving power | - |
dc.subject.keywordPlus | Testing | - |
dc.subject.keywordPlus | Fast adaptations | - |
dc.subject.keywordPlus | Input image | - |
dc.subject.keywordPlus | Low resolution images | - |
dc.subject.keywordPlus | Metalearning | - |
dc.subject.keywordPlus | Natural images | - |
dc.subject.keywordPlus | Single images | - |
dc.subject.keywordPlus | Super resolution | - |
dc.subject.keywordPlus | Test images | - |
dc.subject.keywordPlus | Image enhancement | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Meta-learning | - |
dc.subject.keywordAuthor | Patch recurrence | - |
dc.subject.keywordAuthor | Single-image super-resolution | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-030-58583-9_45 | - |
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