Research map / Probabilistic and scientific modeling / Variational and generative sampling
Rethinking Controllable Variational Autoencoders
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DynamicVAE's incremental PI controller, moving average, and hybrid annealing | VAE · dSprites | RMIG: 0.4295 -> 0.4781 | Helped |
| Use DynamicVAE's controlled KL schedule | VAE · dSprites | MIG: 0.5233 -> 0.5578 | Helped |
| Use DynamicVAE's controlled KL schedule | VAE · dSprites | BetaVAE Score: 0.9608 -> 0.9981 | Helped |
| Replace positional PI control without large β(0) initialization with incremental PI control initialized to large β(0) (ablation) | DynamicVAE · dSprites | RMIG: 0.4641 ± 0.0240 -> 0.4781 ± 0.0172 | Helped |
| Add a ramp function to step-function annealing (ablation) | DynamicVAE · dSprites | RMIG: 0.4555 ± 0.0355 -> 0.4781 ± 0.0172 | Helped |
| Use moving-average feedback instead of instantaneous KL-divergence (ablation) | DynamicVAE · dSprites | RMIG: 0.4570 ± 0.0182 -> 0.4781 ± 0.0172 | Helped |
Closest papers by meaning
- ControlVAE: Controllable Variational Autoencoder (ICML 2020 · similarity 0.94)
- Guided Variational Autoencoder for Disentanglement Learning (CVPR 2020 · similarity 0.90)
- Property Controllable Variational Autoencoder via Invertible Mutual Dependence (ICLR 2021 · similarity 0.89)
- Why do Variational Autoencoders Really Promote Disentanglement? (ICML 2024 · similarity 0.89)
- $\alpha$TC-VAE: On the relationship between Disentanglement and Diversity (ICLR 2024 · similarity 0.88)
- Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders (ICLR 2024 · similarity 0.88)
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