The document discusses the proposal of 'pro-VLAE', a progressive learning approach to enhance the learning and disentangling of hierarchical representations in variational autoencoders (VAEs). It demonstrates improved disentanglement metrics and superior performance compared to existing models like B-VAE and VLAE by progressively growing the network's capacity. Future work aims to strengthen guidance for information allocation across different abstraction levels.

1. Progressive Learning and Disentanglement of
Hierarchical Representations
Hwang seung hyun
Yonsei University Severance Hospital CCIDS
Rochester Institute of Technology | ICLR 2020
2020.08.23

2. Introduction Related Work Methods and
Experiments
01 02 03
Conclusion
04
Yonsei Unversity Severance Hospital CCIDS
Contents

3. Pro-VLAE
Introduction – Background
• Learning rich representation from data is
important for deep generative models like
VAE
• VAE has not been able to fully utilize the
depth of NNs → Encoder is able to extract
high-level representations helpful for
discriminative tasks, but generation requires
the preservation of all generative factors at
different abstraction levels
• It is difficult to extract and disentangle all
generative factors at once, especially at
different abstraction levels
Introduction / Related Work / Methods and Experiments / Conclusion
01
[Variational Auto Encoder]
[Latent space Disentanglement]

4. Pro-VLAE
Introduction – Proposal
• Use progressive learning strategies to improve learning and disentangling of
hierarchical representations
- Hierarchical representations are latent representations from different depths of the
inference(encoder) and generation(decoder) model [1] VLAE
- Progressively grow the capacity of the network.
→ Named it pro-VLAE
Introduction / Related Work / Methods and Experiments / Conclusion
02[1] Shengjia Zhao, Jiaming Song, and Stefano Ermon. Learning hierarchical features from deep generative models. In International Conference on Machine Learning, pp. 4091–
4099, 2017.

5. Pro-VLAE
Introduction – Contribution
• Showed improved disentanglement on several datasets using three disentanglement
metrics
• Presented both qualitative and quantitative evidence that pro-VLAE is able to first learn
the most abstract representations and then progressively disentangle existing factors.
• Showed superior disentanglement performance compared to B-VAE, VLAE, and the
teacher-student strategy.
Introduction / Related Work / Methods and Experiments / Conclusion
03

6. Related Work
Introduction / Related Work / Methods and Experiments / Conclusion
04
Variational Autoencoder
[Encoder]
[Decoder]
* Images imported from https://itrepo.tistory.com/48

7. Related Work
Introduction / Related Work / Methods and Experiments / Conclusion
05
Variants of VAE
VLAE [1]
B-VAE [2]
[1] Shengjia Zhao, Jiaming Song, and Stefano Ermon. Learning hierarchical features from deep generative models. In International Conference on Machine Learning, pp. 4091–
4099, 2017.
[2] Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. beta-vae: Learning basic visual
concepts with a constrained variational framework. In International Conference on Learning Representations, 2017.
• Learn independent hierarchical representation
from different levels of abstraction
[Variational Ladder AutoEncoder]
[Beta - VAE]
• When B>1, applies a stronger constraint on the latent bottleneck
and limits the representation capacity of z
• Higher ‘B’ value encourages more efficient latent encoding and
disentanglement, in change of reconstruction quality.

8. Related Work
Introduction / Related Work / Methods and Experiments / Conclusion
06
Progressive Learning
[3] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. In International Conference on Learning
Representations, 2018.
[4] Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga SorkineHornung, and Christopher Schroers. A fully progressive approach to single-image
superresolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 864–873, 2018.
• A small number of works have demonstrated the ability to simply grow the
architecture of GAN’s from a shallow network with limited capacity for generating
low-resolution images, to a deep network capable of generating super-resolution
images. [3], [4]

9. Related Work
Introduction / Related Work / Methods and Experiments / Conclusion
07
Teacher-student strategy
• Teacher-student strategy was used to progressively grow the dimension of the
latent representations in VAE [5]
• After a teacher is trained to correctly disentangle attributes, a student model will
be trained to improve the visual quality of the reconstruction
[5] Jose Lezama. Overcoming the disentanglement vs reconstruction trade-off via jacobian supervision. ´ In International Conference on Learning Representations, 2019.

10. Methods and Experiments
Model : VAE with hierarchical representations
Introduction / Related Work / Methods and Experiments / Conclusion
08
• Decompose z into {z1, z2, …zL} with each z from different abstraction levels.
• z are independent and each represents generative factors not captured in other
levels
• Define an inference model to approximate the posterior
• Final goal of model is to maximize a modified evidence lower bound(ELBO)

11. Methods and Experiments
Progressive learning of hierarchical representation
Introduction / Related Work / Methods and Experiments / Conclusion
09
• Learn the latent variables progressively from the highest to the lowest (
• At each progressive step, move down one abstraction level and grow the inference model
by introducing new latent code
• Simultaneously, grow the decoder

12. Methods and Experiments
Implementation Strategies
Introduction / Related Work / Methods and Experiments / Conclusion
10
(1) Fade In
- fade in coefficient alpha when growing new components into encoder and decoder
(2) Regularization term on Z
- applying KL penalty to all latent variables that have not been used in the generation at
progressive step s

13. Methods and Experiments
Code
Introduction / Related Work / Methods and Experiments / Conclusion
11
[1] Inference [2] Sampling
[3] Generation
[4] Latent Loss
[5] Reconstruction Loss

14. Methods and Experiments
Introduction / Related Work / Methods and Experiments / Conclusion
12
[7] Optimize
[6] Training
Code

15. Methods and Experiments
Disentanglement Metric
Introduction / Related Work / Methods and Experiments / Conclusion
13
(1) MIG (Mutual Information Gap) [6]
[6] Jose Lezama. Overcoming the disentanglement vs reconstruction trade-off via jacobian supervision. ´ In International Conference on Learning Representations, 2019.
(2) MIG-sup (supplement MIG)
• Single factor can have high mutual information with multiple latent variables
• Measure the difference between the top two latent variables with highest mutual information
• If different generative factors are entangled into the same latent dimension, MIG score doesn’t work
• Propose MIG-sup to recognize the entanglement of multiple generative factors in same latent
dimension
Latent A Latent B
Generative
factor A
Generative
factor B
[Generative Factor K]
[Latent Space J]

16. Methods and Experiments
Experiments
Introduction / Related Work / Methods and Experiments / Conclusion
14

17. Methods and Experiments
Experiments
Introduction / Related Work / Methods and Experiments / Conclusion
15

18. Methods and Experiments
Experiments
Introduction / Related Work / Methods and Experiments / Conclusion
16

19. Methods and Experiments
Experiments
Introduction / Related Work / Methods and Experiments / Conclusion
17

20. Methods and Experiments
Experiments
Introduction / Related Work / Methods and Experiments / Conclusion
18

21. Conclusion
Introduction / Related Work / Methods and Experiments / Conclusion
• Presented a progressive strategy for learning and disentangling
hierarchical representations
• Model learn independent representations from high-to-low levels of
abstraction by progressively growing the capacity of the VAE deep to
shallow
• Future work is to include stronger guidance for allocating information
across the hierarchy of abstraction levels
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