Review : Progressive learning and Disentanglement of hierarchical representations
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
The Codex of Business Writing Software for Real-World Solutions 2.pptx
Progressive learning and Disentanglement of hierarchical representations
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
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]
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
19