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Presented by: Mingyuan Zhou Duke University, ECE Feb 22, 2013

Large Scale Variational Bayesian Inference for Structured Scale Mixture Models Young Jun Ko and Matthias Seeger ICML 2012. Presented by: Mingyuan Zhou Duke University, ECE Feb 22, 2013. Introduction. Natural image statistics exhibit hierarchical dependencies across multiple scales.

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Presented by: Mingyuan Zhou Duke University, ECE Feb 22, 2013

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  1. Large Scale Variational Bayesian Inference forStructured Scale Mixture ModelsYoung Jun Ko and Matthias SeegerICML 2012 Presented by: Mingyuan Zhou Duke University, ECE Feb 22, 2013

  2. Introduction • Natural image statistics exhibit hierarchical dependencies across multiple scales. • Non-factorial latent tree models. • A large scale approximate Bayesian inference algorithm for linear models with non-factorial (latent tree-structured) scale mixture priors. • Experimental results on a range of denoising and inpainting problems demonstrate substantially improved performance compared to MAP estimation or to inference with factorial priors.

  3. Structured Image Model • Impose sparsity

  4. Structured Image Model • Non-factorial scale mixture model: • Output:

  5. Example

  6. Large Scale Variational Inference • Due to strong dependencies between components of u and s, factorial assumption might be restrictive. • Iterative decoupling • Decouple • Decouple mean and covariance components of • Prior:

  7. Large Scale Variational Inference • VB

  8. Large Scale Variational Inference

  9. Image denoising

  10. Image inpainting

  11. Conclusions

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