awesome-unsupervised-imaging
github.com/andrewwango/awesome-unsupervised-imaging ↗Resources for solving imaging inverse problems using deep learning without ground truth
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15
Curated Resources
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FrameworksMetricsFoundations
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Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me generative models resources from awesome-unsupervised-imaging"
Installation instructions →What's inside
Frameworks
- CoIL paperGenerative models
- Image transformations for Equivariant Imaging | DeepInverseEquivariant imaging
DeepInverse
- Imaging inverse problems with adversarial networks | DeepInverseGenerative models
DeepInverse
- Kamilov 2022 DMBA OverviewGenerative models
- Self-supervised denoising with the Neighbor2Neighbor loss | DeepInverseMulti-operator algorithms
DeepInverse
- Self-supervised denoising with the SURE loss | DeepInverseMulti-operator algorithms
DeepInverse
Foundations
- Group TheoryGroup theory
- Introduction to Groups, Representations, Equivariance and Geometric Deep LearningGroup theory
Showing a sample of 15 resources. View the full list on GitHub →