Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images Article Swipe
Nanqing Dong
,
Matteo Maggioni
,
Yongxin Yang
,
Eduardo Pérez-Pellitero
,
Aleš Leonardis
,
Steven McDonagh
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2022/406
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2022/406
This paper is concerned with contrastive learning (CL) for low-level image restoration and enhancement tasks. We propose a new label-efficient learning paradigm based on residuals, residual contrastive learning (RCL), and derive an unsupervised visual representation learning framework, suitable for low-level vision tasks with noisy inputs. While supervised image reconstruction aims to minimize residual terms directly, RCL alternatively builds a connection between residuals and CL by defining a novel instance discrimination pretext task, using residuals as the discriminative feature. Our formulation mitigates the severe task misalignment between instance discrimination pretext tasks and downstream image reconstruction tasks, present in existing CL frameworks. Experimentally, we find that RCL can learn robust and transferable representations that improve the performance of various downstream tasks, such as denoising and super resolution, in comparison with recent self-supervised methods designed specifically for noisy inputs. Additionally, our unsupervised pre-training can significantly reduce annotation costs whilst maintaining performance competitive with fully-supervised image reconstruction.
Related Topics
Concepts
Artificial intelligence
Computer science
Discriminative model
Residual
Pattern recognition (psychology)
Margin (machine learning)
Machine learning
Task (project management)
Feature learning
Supervised learning
Unsupervised learning
Feature (linguistics)
Image (mathematics)
Artificial neural network
Algorithm
Linguistics
Philosophy
Management
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.24963/ijcai.2022/406
- https://www.ijcai.org/proceedings/2022/0406.pdf
- OA Status
- bronze
- Cited By
- 4
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285604901
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285604901Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.24963/ijcai.2022/406Digital Object Identifier
- Title
-
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy ImagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-01Full publication date if available
- Authors
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Nanqing Dong, Matteo Maggioni, Yongxin Yang, Eduardo Pérez-Pellitero, Aleš Leonardis, Steven McDonaghList of authors in order
- Landing page
-
https://doi.org/10.24963/ijcai.2022/406Publisher landing page
- PDF URL
-
https://www.ijcai.org/proceedings/2022/0406.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
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https://www.ijcai.org/proceedings/2022/0406.pdfDirect OA link when available
- Concepts
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Artificial intelligence, Computer science, Discriminative model, Residual, Pattern recognition (psychology), Margin (machine learning), Machine learning, Task (project management), Feature learning, Supervised learning, Unsupervised learning, Feature (linguistics), Image (mathematics), Artificial neural network, Algorithm, Linguistics, Philosophy, Management, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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