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

Rethinking Controllable Variational Autoencoders

CVPR 2022 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Regularizing VAE latent information · 15 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use DynamicVAE's incremental PI controller, moving average, and hybrid annealingVAE · dSpritesRMIG: 0.4295 -> 0.4781Helped
Use DynamicVAE's controlled KL scheduleVAE · dSpritesMIG: 0.5233 -> 0.5578Helped
Use DynamicVAE's controlled KL scheduleVAE · dSpritesBetaVAE Score: 0.9608 -> 0.9981Helped
Replace positional PI control without large β(0) initialization with incremental PI control initialized to large β(0) (ablation)DynamicVAE · dSpritesRMIG: 0.4641 ± 0.0240 -> 0.4781 ± 0.0172Helped
Add a ramp function to step-function annealing (ablation)DynamicVAE · dSpritesRMIG: 0.4555 ± 0.0355 -> 0.4781 ± 0.0172Helped
Use moving-average feedback instead of instantaneous KL-divergence (ablation)DynamicVAE · dSpritesRMIG: 0.4570 ± 0.0182 -> 0.4781 ± 0.0172Helped

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