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Denoising Criterion for Variational Auto-Encoding Framework

AAAI 2017 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Regularizing VAE latent information · 138 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add salt-and-pepper input corruption at noise level 5VAE · Binarized MNISTnegative variational lower bound: 96.14 ± 0.09 -> 95.52 ± 0.12*Helped
Add salt-and-pepper input corruption at noise level 5IWAE · Binarized MNISTnegative variational lower bound: 94.36 ± 0.07 -> 93.67 ± 0.10*Helped
Use noise level 15 instead of no input corruption (ablation)VAE · Binarized MNISTnegative variational lower bound: 96.14 ± 0.09 -> 96.83 ± 0.17Hurt
Add Gaussian input corruption at noise level 7.5VAE · Frey Facenegative variational lower bound: 1304.79 ± 5.71 -> 1293.07 ± 5.03Helped
Add Gaussian input corruption at noise level 5IWAE · Frey Facenegative variational lower bound: 1318.04 ± 2.83 -> 1333.44 ± 2.74*Hurt
Add salt-and-pepper input corruption at noise level 5VAE with GRU inference network · Binarized MNISTnegative variational lower bound: 96.07 ± 0.17 -> 94.30 ± 0.09*Helped
Add salt-and-pepper input corruption at noise level 10IWAE with GRU inference network · Binarized MNISTnegative variational lower bound: 93.94 ± 0.06 -> 92.84 ± 0.07*Helped
Increase the number of corruption samples M from 1 to 10 (ablation)VAE with denoising criterion · MNISTnegative log-likelihood: 94.97, 94.44, and 94.39Helped
Increase the number of importance samples M from 1 to 10 (ablation)IWAE with denoising criterion · MNISTnegative log-likelihood: 93.17, 92.89, and 92.85Helped
Replace the feedforward inference network with a GRU (ablation)VAE · MNISTnegative variational lower boundHurt
Use data augmentation instead of denoising (ablation)VAE · MNISTnegative log-likelihood: 94.32 ± 0.37 -> 93.88 ± 0.08Helped
Use data augmentation instead of denoising (ablation)IWAE · MNISTnegative log-likelihood: 93.83 ± 0.06 -> 92.51 ± 0.07Hurt

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