β-VAE — what does the β hyperparameter do?
hardAnswer
- Modifies ELBO to . β > 1 pushes KL harder → more independent latent factors → disentangled representations (each latent dim captures one factor: pose, color, size).
- Cost: worse reconstruction.
- Trade-off between disentanglement (large β) and fidelity (small β).
- Foundation of disentangled representation learning; extended by FactorVAE, β-TCVAE.
Check yourself — multiple choice
- Random
- ELBO = recon - β·KL; β > 1 → more independent latents (disentanglement) at cost of reconstruction; FactorVAE / β-TCVAE extend
- Same as VAE
- Not real
β-VAE: β on KL → disentanglement vs reconstruction tradeoff.
#representation-learning#deep-learning
Practise Unsupervised Learning
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