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Research map / Probabilistic and scientific modeling / Variational and generative sampling

Generalization Gap in Amortized Inference

NeurIPS 2022 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Structured latent VAEs · 3 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Train amortized inference with reverse half-asleep objective instead of standard VAE inferenceVAE · Binary MNIST testBPD: 0.200 -> 0.187Helped
Train amortized inference with reverse half-asleep objective instead of standard VAE inferenceVAE · Grey MNIST testBPD: 1.543 -> 1.513Helped
Train amortized inference with reverse half-asleep objective instead of standard VAE inferenceVAE · CIFAR10 testBPD: 4.592 -> 4.572Helped
Train amortized inference with reverse half-asleep objectiveVAE · CIFAR100 testBPD: 5.288 to 5.275Helped
Add a denoising regularizer with σ=0.1, σ=0.2, or σ=0.4VAE amortized inference · Binary MNIST and Grey MNIST testBPD: Binary MNIST: 0.200 -> 0.195, 0.192, 0.191; Grey MNIST: 1.543 -> 1.527, 1.519, 1.515Helped
Increase denoising noise to σ=0.8 or σ=1.0VAE amortized inference · Binary MNIST and Grey MNIST testBPD: Binary MNIST: 0.200 -> 0.196, 0.201; Grey MNIST: 1.543 -> 1.545, 1.550Hurt
Add a denoising regularizer with σ=0.1VAE amortized inference · CIFAR10 testBPD: 4.592 -> 4.591Helped
Add a denoising regularizer with σ=0.2, σ=0.4, σ=0.8, or σ=1.0VAE amortized inference · CIFAR10 testBPD: 4.592 -> 4.598, 4.614, 4.651, 4.667Hurt
Use reverse sleep rather than reverse half-asleep (α=0.5) (ablation)VAE amortized inference · Binary MNIST testBPDHurt
Use standard amortized inference rather than reverse half-asleep (α=0.5) (ablation)VAE amortized inference · Binary MNIST testBPDHurt
Use reverse half-asleep training from the beginning rather than post-hoc (ablation)VAE · Binary MNIST testELBOHelped
Use reverse half-asleep training from the beginning rather than standard VAE training (ablation)VAE · Grey MNIST testELBOHurt

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