Research map / Probabilistic and scientific modeling / Variational and generative sampling
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed structured semi-supervised VAE objective | MNIST and SVHN · MNIST and SVHN | classification error | No clear effect |
| Modestly increase the relative weight of labelled examples | Structured semi-supervised VAE · MNIST and SVHN | test classification error | Helped |
| Increase the relative weight of labelled examples beyond the useful range | Structured semi-supervised VAE · MNIST and SVHN | test classification error | Hurt |
| Use the proposed structured model to infer digit counts and reconstruct inputs | Multi-MNIST nested model · Multi-MNIST | count accuracy | Helped |
| Add partial supervision for interpretable latent variables | Partially specified VAE · MNIST and SVHN | semantic disentanglement | Helped |
Closest papers by meaning
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- PROGRESSIVE LEARNING AND DISENTANGLEMENT OF HIERARCHICAL REPRESENTATIONS (ICLR 2020 · similarity 0.91)
- Guided Variational Autoencoder for Disentanglement Learning (CVPR 2020 · similarity 0.91)
- Demystifying Inductive Biases for (Beta-)VAE Based Architectures (ICML 2021 · similarity 0.91)
- Disentangling Disentanglement in Variational Autoencoders (ICML 2019 · similarity 0.90)
- $\alpha$TC-VAE: On the relationship between Disentanglement and Diversity (ICLR 2024 · similarity 0.90)
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