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Intro
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Classroom Contents
On Bayesian Models with Networks for Reconstruction and Detection
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- 1 Intro
- 2 Outline
- 3 Acknowledgements
- 4 Examples: Image enhancement
- 5 Posterior distribution
- 6 Cartoon representation
- 7 Why use generative models for analyzing images?
- 8 Principal component analysis
- 9 Variational auto-encoders
- 10 MRI acquisition
- 11 Bayesian model for image reconstruction
- 12 MAP estimation with network prior
- 13 Advantage of generative modeling: decoupling
- 14 A distinction in the concept of "prior"
- 15 Unsupervised outlier detection
- 16 Restoration for outlier detection
- 17 Experimental details
- 18 ROC curves
