Research map / Probabilistic and scientific modeling / Variational and generative sampling
Message Passing Stein Variational Gradient Descent
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Markov-blanket-localized kernels and coordinate-wise message passing instead of a global SVGD kernel | SVGD · BSD test images, σ_n = 10 | avg. PSNR: 31.58 -> 32.09 | Helped |
| Use Markov-blanket-localized kernels and coordinate-wise message passing instead of a global SVGD kernel | SVGD · BSD test images, σ_n = 20 | avg. PSNR: 27.86 -> 28.21 | Helped |
| Use Markov-blanket-localized kernels and coordinate-wise message passing instead of a global SVGD kernel | SVGD · BSD test images, σ_n = 10 | avg. SSIM: 0.894 -> 0.905 | Helped |
| Use Markov-blanket-localized kernels and coordinate-wise message passing instead of a global SVGD kernel | SVGD · BSD test images, σ_n = 20 | avg. SSIM: 0.766 -> 0.808 | Helped |
| Use the Multi-Kernel rather than the Single-Kernel design (ablation) | MP-SVGD · Synthetic pairwise MRF | RMSE | Helped |
| Use the Multi-Kernel rather than the Single-Kernel design (ablation) | MP-SVGD · Synthetic pairwise MRF, varying dimensionality | PAMRF | Helped |
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