Slides presented in the All Japan Computer Vision Study Group on May 15, 2022. Methods for disentangling the relationship between multimodal data are discussed.
1. AGREEMENT
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Tejero-de-Pablos A. (2022) “VAEs for multimodal disentanglement”. All Japan Computer Vision Study Group.
7. What is a VAE?
• Auto-encoder • Variational auto-encoder
With the proper regularization:
8. There is more!
• Vector Quantized-VAE
Quantize the bottleneck using a discrete codebook
There are a number of algorithms (like transformers) that are designed to work on discrete data, so we
would like to have a discrete representation of the data for these algorithms to use.
Advantages of VQ-VAE:
- Simplified latent space (easier to train)
- Likelihood-based model: do not suffer from the
problems of mode collapse and lack of diversity
- Real world data favors a discrete representation
(number of images that make sense is kind of finite)
9. Why are VAEs cool?
• Usage of VAEs (state-of-the-art)
Multimodal generation (DALL-E)
Representation learning, latent space disentanglement
11. Today I’m introducing:
1) Shi, Y., Paige, B., & Torr, P. (2019). Variational mixture-of-experts autoencoders for multi-modal
deep generative models. Advances in Neural Information Processing Systems, 32.
2) Lee, M., & Pavlovic, V. (2021). Private-shared disentangled multimodal VAE for learning of
latent representations. Conference on Computer Vision and Pattern Recognition (pp. 1692-
1700).
3) Joy, T., Shi, Y., Torr, P. H., Rainforth, T., Schmon, S. M., & Siddharth, N. (2022). Learning
Multimodal VAEs through Mutual Supervision. International Conference on Learning
Representations.
12. Motivation and goal
• Importance of multimodal data
Learning in the real world involves multiple perspectives: visual, auditive, linguistic
Understanding them individually allows only a partial learning of concepts
• Understanding how different modalities work together is not trivial
A similar joint-embedding process happens in the brain for reasoning and understanding
• Multimodal VAE facilitate representation learning on data with multiple views/modalities
Capture common underlying factors between the modalities
13. Motivation and goal
• Normally, only the shared aspects of modalities are modeled
The private information of each modality is totally LOST
E.g., image captioning
• Leverage VAE’s latent space for disentanglement
Private spaces are leveraged for modeling the disjoint properties of each
modality, and cross-modal generation
• Basically, such disentanglement can be used as:
An analytical tool to understand how modalities intertwine
A way of cross-generating modalities
14. Motivation and goal
• [1] and [2] propose a similar methodology
According to [1], a true multimodal generative model should meet four criteria:
Today I will introduce [2] (most recent), and explain briefly the differences with [3]
15. Dataset
• Digit images: MNIST & SVHN
- Shared features: Digit class
- Private features: Number style, background, etc.
Image domains as different modalities?
• Flower images and text description: Oxford-102 Flowers
- Shared features: Words and image features present in both
modalities
- Private features: Words and image features exclusive from
their modality
MNIST
SVHN
16. Related work
• Multimodal generation and joint multimodal VAEs (e.g., JMVAE, MVAE)
The learning of a common disentangled embedding (i.e., private-shared) is often ignored
Only some works in image-to-image translation separate ”content” (~shared) and ”style” (~private) in the
latent space (e.g., via adversarial loss)
Exclusively for between-image modalities: Not suitable for different modalities such as image and text
• Domain adaptation
Learning joint embeddings of multimodal observations
17. Proposed method: DMVAE
• Generative variational model: Introducing separate shared and private spaces
Usage: Cross-generation (analytical tool)
• Representations induced using pairs of individual modalities (encoder, decoder)
• Consistency of representations via Product of Experts (PoE). For a number of modalities N:
𝑞 𝑧! 𝑥", 𝑥#, ⋯ , 𝑥$ ∝ 𝑝(𝑧!) *
%&"
$
𝑞(𝑧!|𝑥%)
In VAE, inference networks and priors assume conditional Gaussian forms
𝑝 𝑧 = 𝑁 𝑧 0, 𝐼 , 𝑞 𝑧 𝑥% = 𝑁 𝑧 𝜇%, 𝐶%
𝑧"~𝑞'!
𝑧 𝑥" , 𝑧#~𝑞'"
𝑧 𝑥#
𝑧" = 𝑧(!
, 𝑧!!
, 𝑧# = 𝑧("
, 𝑧!"
We want: 𝑧) = 𝑧!!
= 𝑧!"
→ PoE
18. Proposed method: DMVAE
• Reconstruction inference
PoE-induced shared inference allows for inference when one or more modalities are missing
Thus, we consider three reconstruction tasks:
- Reconstruct both modalities at the same time: 𝑥", 𝑥# → 4
𝑥", 4
𝑥# 𝑧(!
, 𝑧("
, 𝑧)
- Reconstruct a single modality from its own input: 𝑥" → 4
𝑥" 𝑧(!
, 𝑧) or 𝑥# → 4
𝑥# 𝑧("
, 𝑧)
- Reconstruct a single modality from the opposite modality’s input: 𝑥# → 4
𝑥" 𝑧(!
, 𝑧) or 𝑥" → 4
𝑥# 𝑧("
, 𝑧)
• Loss function
Accuracy of reconstruction for jointly learned shared latent + KL-divergence of each normal distribution
Accuracy of cross-modal and self reconstruction + KL-divergence
19. Experiments: Digits (image-image)
• Evaluation
Qualitative: Cross-generation between modalities
Quantitative: Accuracy of the cross-generated images using a pre-trained classifier for each modality
- Joint: A sample from zs generates two image modalities that must be assigned the same class
Input
Output for
different
samples of zp2
Input
Output for
different
samples of zp1
21. Experiments: Flowers (image-text)
• This task is more complex
Instead of the image-text, the intermediate features are reconstructed
• Quantitative evaluation
Class recognition (image-to-text) and cosine-similarity retrieval (text-to-image) on the shared latent space
• Qualitative evaluation
Retrieval
22. Conclusions
• Multimodal VAE for disentangling private and shared spaces
Improve the representational performance of multimodal VAEs
Successful application to image-image and image-text modalities
• Shaping a latent space into subspaces that capture the private-shared aspects of the
modalities
“is important from the perspective of downstream tasks, where better decomposed representations are more
amenable for using on a wider variety of tasks”
23. [3] Multimodal VAEs via mutual supervision
• Main differences with [1] and [2]
A type of multimodal VAE, without private-shared disentanglement
Does not rely on factorizations such as MoE or PoE for modeling modality-shared information
Instead, it repurposes semi-supervised VAEs for combining inter-modality information
- Allows learning from partially-observed modalities (Reg. = KL divergence)
• Proposed method: Mutually supErvised Multimodal vaE (MEME)
24. [3] Multimodal VAEs via mutual supervision
• Qualitative evaluation
Cross-modal generation
• Quantitative evaluation
Coherence: Percentage of matching predictions of the cross-generated modality using a pretrained classifier
Relatedness: Wassertein Distance between the representations of two modalities (closer if same class)
26. Final remarks
• VAE not only for generation but also for reconstruction and disentanglement tasks
Recommended textbook: “An Introduction to Variational Autoencoders”, Kingma & Welling
• Private-shared latent spaces as an effective tool for analyzing multimodal data
• There is still a lot of potential for this research
It has been only applied to a limited number of multimodal problems
• このテーマに興味のある博士課程の学生 → インターン募集中
https://www.cyberagent.co.jp/news/detail/id=27453
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共同研究も大歓迎！
