Methods › Computer Vision › Face Restoration Models › ISPL
Implicit Subspace Prior Learning
ISPL
Introduced by Lingbo Yang et al. in Implicit Subspace Prior Learning for Dual-Blind Face Restoration
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Implicit Subspace Prior Learning, or ISPL, is a framework to approach dual-blind face restoration, with two major distinctions from previous restoration methods: 1) Instead of assuming an explicit degradation function between LQ and HQ domain, it establishes an implicit correspondence between both domains via a mutual embedding space, thus avoid solving the pathological inverse problem directly. 2) A subspace prior decomposition and fusion mechanism to dynamically handle inputs at varying degradation levels with consistent high-quality restoration results.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile 4 Jun 2022 · 1 repository · arXiv:2206.01944
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Robust Meta-learning with Noise via Eigen-Reptile 1 Jan 2021 · 0 repositories
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Implicit Subspace Prior Learning for Dual-Blind Face Restoration 12 Oct 2020 · 1 repository · arXiv:2010.05508
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Few-Shot Learning | 2 |
| Meta-Learning | 2 |
| Blind Face Restoration | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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