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

Variational Autoencoders Pursue PCA Directions (by Accident)

CVPR 2019 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Regularizing VAE latent information · 113 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace the diagonal posterior with a full-covariance posterior (ablation)β-VAE · dSpritesDtO: 0.76 ± 0.08 -> 1.73 ± 0.14Hurt
replace the diagonal posterior with a full-covariance posterior (ablation)β-VAE · Synth. Lin.DtO: 0.00 ± 0.00 -> 0.34 ± 0.35Hurt
replace the diagonal posterior with a full-covariance posterior (ablation)β-VAE · Synth. Non-Lin.DtO: 0.18 ± 0.02 -> 0.55 ± 0.02Hurt
replace the diagonal posterior with a full-covariance posterior (ablation)β-VAE · Synth. Non-Lin.Disentanglement score: 0.73 ± 0.16 -> 0.42 ± 0.24Hurt
use a diagonal posterior rather than a full-covariance posterior (ablation)β-VAE · dSpritesDisentanglement score: 0.12 ± 0.06 -> 0.33 ± 0.15Helped
use a diagonal posterior rather than a full-covariance posterior (ablation)β-VAE · Synth. Lin.Disentanglement score: 0.71 ± 0.31 -> 0.99 ± 0.01Mixed

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