1. Learning Hierarchical Features from Generative Models
Presenter: Haotian Xu

2. Agenda
Motivation
Model
Experimental results
Discussion

3. Agenda
Motivation
Model
Experimental results
Discussion

4. Variational Autoencoder (VAE)

5. Hierarchical VAE
VAEs stacked on top of each other

6. Hierarchical VAE Advantages
Improve Evidence Lower Bound (ELBO) and decrease reconstruction error
Validated
Might learn a feature hierarchy similar to CNNs
difficult to learn a meaningful hierarchy when there are many layers of latent variables

7. Hierarchical VAE Limitation
under ideal conditions, layers above the first one are redundant.
CNN redundant?

8. Agenda
Motivation
Model
Experimental results
Discussion

9. Model Variational Ladder Autoencoder (VLAE)
use shallow networks to express low-level, simple features
use deep networks to express high-level, complex features

10. Agenda
Motivation
Model
Experimental results
Discussion

11. Generated results

12. Agenda
Motivation
Model
Experimental results
Discussion

13. Discussion
In terms of learning hierarchical features, deeper is not always better for VAEs
The advantage of learning a hierarchy is in the introduction of structure in the features, such as hierarchy or disentanglement