pcaGAN: Improving Posterior-Sampling cGANs via Principal Component Regularization Article Swipe
Matthew C Bendel
,
R. Badlishah Ahmad
,
Philip Schniter
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.52202/079017-4406
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.52202/079017-4406
In ill-posed imaging inverse problems, there can exist many hypotheses that fit both the observed measurements and prior knowledge of the true image. Rather than returning just one hypothesis of that image, posterior samplers aim to explore the full solution space by generating many probable hypotheses, which can later be used to quantify uncertainty or construct recoveries that appropriately navigate the perception/distortion trade-off. In this work, we propose a fast and accurate posterior-sampling conditional generative adversarial network (cGAN) that, through a novel form of regularization, aims for correctness in the posterior mean as well as the trace and K principal components of the posterior covariance matrix. Numerical experiments demonstrate that our method outperforms contemporary cGANs and diffusion models in imaging inverse problems like denoising, large-scale inpainting, and accelerated MRI recovery. The code for our model can be found here: https://github.com/matt-bendel/pcaGAN.
Related Topics
Concepts
Regularization (linguistics)
Correctness
Inpainting
Principal component analysis
Computer science
Artificial intelligence
Sampling (signal processing)
Covariance
Inverse
Mathematics
Inverse problem
Posterior probability
Pattern recognition (psychology)
Image (mathematics)
Algorithm
Computer vision
Statistics
Bayesian probability
Filter (signal processing)
Mathematical analysis
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.52202/079017-4406
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://doi.org/10.52202/079017-4406Digital Object Identifier
- Title
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pcaGAN: Improving Posterior-Sampling cGANs via Principal Component RegularizationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Matthew C Bendel, R. Badlishah Ahmad, Philip SchniterList of authors in order
- Landing page
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https://doi.org/10.52202/079017-4406Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2411.00605Direct OA link when available
- Concepts
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Regularization (linguistics), Correctness, Inpainting, Principal component analysis, Computer science, Artificial intelligence, Sampling (signal processing), Covariance, Inverse, Mathematics, Inverse problem, Posterior probability, Pattern recognition (psychology), Image (mathematics), Algorithm, Computer vision, Statistics, Bayesian probability, Filter (signal processing), Mathematical analysis, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| best_oa_location.source.issn_l | |
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| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
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| best_oa_location.version | submittedVersion |
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| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
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| best_oa_location.landing_page_url | http://arxiv.org/abs/2411.00605 |
| primary_location.id | doi:10.52202/079017-4406 |
| primary_location.is_oa | False |
| primary_location.source | |
| primary_location.license | |
| primary_location.pdf_url | |
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| primary_location.landing_page_url | https://doi.org/10.52202/079017-4406 |
| publication_date | 2024-01-01 |
| publication_year | 2024 |
| referenced_works_count | 0 |
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