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Constraining Variational Inference with Geometric Jensen-Shannon Divergence

NeurIPS 2020 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Structured latent VAEs · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the dual skew-geometric JS divergence with a tuned αVAE · MNIST, Fashion-MNIST, dSprites, and Chairsreconstruction error (MSE): KL(q(z|x)||p(z)): 8.46, 11.98, 13.55, 12.27; JS^G_α*: 7.34, 9.58, 4.97, 11.64Helped
Use JS^G_α with a tuned α instead of KL(q(z|x)||p(z))VAE · MNIST, Fashion-MNIST, dSprites, and Chairsreconstruction error (MSE): 8.46 -> 7.52; 11.98 -> 10.04; 13.55 -> 5.54; 12.27 -> 11.95Helped
Use β=0.25 instead of the standard KL baseline (ablation)β-VAE · MNISTreconstruction error (MSE): 8.46 -> 8.09Helped
Use β=0.25 instead of the standard KL baseline (ablation)β-VAE · Fashion-MNISTreconstruction error (MSE): 11.98 -> 9.07Helped
Use β=0.25 instead of the standard KL baseline (ablation)β-VAE · dSpritesreconstruction error (MSE): 13.55 -> 10.39Helped
Use β=0.25 instead of the standard KL baseline (ablation)β-VAE · Chairsreconstruction error (MSE): 12.27 -> 14.09Hurt
Set α=0.5 rather than the lowest-error skew reported for JS^G_α (ablation)JS^G_α VAE · MNIST, Fashion-MNIST, dSprites, and Chairsreconstruction error (MSE): 7.52 -> 9.87; 10.04 -> 11.29; 5.54 -> 9.89; 11.95 -> 13.57Hurt
Increase α above 0.5 in JS^G_α (ablation)JS^G_α VAE · MNIST and Fashion-MNISTreconstruction error (MSE): MNIST: 9.87 -> 11.87; Fashion-MNIST: 11.29 -> 13.21Hurt
Use JS^G_α* for higher generative performance rather than its low-error settingsVAE · MNIST and Fashion-MNISTmodel evidenceMixed

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