Research map / Probabilistic and scientific modeling / Variational and generative sampling
Variational Autoencoders Pursue PCA Directions (by Accident)
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace the diagonal posterior with a full-covariance posterior (ablation) | β-VAE · dSprites | DtO: 0.76 ± 0.08 -> 1.73 ± 0.14 | Hurt |
| replace the diagonal posterior with a full-covariance posterior (ablation) | β-VAE · Synth. Lin. | DtO: 0.00 ± 0.00 -> 0.34 ± 0.35 | Hurt |
| replace the diagonal posterior with a full-covariance posterior (ablation) | β-VAE · Synth. Non-Lin. | DtO: 0.18 ± 0.02 -> 0.55 ± 0.02 | Hurt |
| replace the diagonal posterior with a full-covariance posterior (ablation) | β-VAE · Synth. Non-Lin. | Disentanglement score: 0.73 ± 0.16 -> 0.42 ± 0.24 | Hurt |
| use a diagonal posterior rather than a full-covariance posterior (ablation) | β-VAE · dSprites | Disentanglement score: 0.12 ± 0.06 -> 0.33 ± 0.15 | Helped |
| use a diagonal posterior rather than a full-covariance posterior (ablation) | β-VAE · Synth. Lin. | Disentanglement score: 0.71 ± 0.31 -> 0.99 ± 0.01 | Mixed |
Closest papers by meaning
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- Embrace the Gap: VAEs Perform Independent Mechanism Analysis (NeurIPS 2022 · similarity 0.91)
- Why do Variational Autoencoders Really Promote Disentanglement? (ICML 2024 · similarity 0.91)
- Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders (ICLR 2024 · similarity 0.90)
- Be a Goldfish: Forgetting Bad Conditioning in Sparse Linear Regression via Variational Autoencoders (ICML 2025 · similarity 0.89)
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