Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
We present a factorized hierarchical variational autoencoder, which learns disentangled and interpretable representations from sequential data without supervision. Specifically, we exploit the multi-scale nature of information in sequential data by formulating it explicitly within a factorized hierarchical graphical model that imposes sequence-dependent priors and sequence-independent priors to different sets of latent variables. The model is evaluated on two speech corpora to demonstrate, qualitatively, its ability to transform speakers or linguistic content by manipulating different sets of latent variables; and quantitatively, its ability to outperform an i-vector baseline for speaker verification and reduce the word error rate by as much as 35% in mismatched train/test scenarios for automatic speech recognition tasks.
Summary
This paper proposes a hierarchical probabilistic generative model for sequential data. The proposed model structure distinguishes between higher-level sequence and lower-level frame latent variables in order to capture multiple scales. A discriminative regularizer is added to the ELBO to encourage diversity across sequence latent variables with the aim of disentangling the latent representations. This work is related to approaches for learning disentangled representations in probabilistic generative models, such as DC-IGN (Kulkarni et al. 2015), InfoGAN (Chen et al. 2016), and Beta-VAE (Higgins et al. 2017). It is also related to hierarchical VAE-style models such as Structured VAEs (Johnson et al. 2016) and the Neural Statistician (Edwards & Storkey 2017) and latent variable models for sequential data including VRNN (Chung et al. 2015), SRNN (Fraccaro et al. 2016) and the hierarchical multiscale RNN of Chung et al. (2017).
Strengths
- Disentanglement in sequential data is not studied sufficiently in current literature
- Seq2Seq model within each frame is sensible
- Paper is overall well-written and clear
Weaknesses
- The proposed hierarchical model makes frame-level independence assumptions
- Discriminative objective is reminiscent of a supervised objective and thus it not clear that the resulting disentanglement of latent representations is in fact unsupervised
- Experiments are somewhat incomplete, both in comparing to baselines and in empirically validating disentanglement
Quality
Two design decisions regarding the graphical model did not make sense to me. First, if z2 is meant to capture sequence-level information, why is it placed inside the frame-level plate? Second, modeling frames independently for a given sequence discards the correlations between frames. Even if this facilitates learning at the frame level, it does not seem to be an entirely appropriate assumption for sequential data. I am wondering if the authors can clarify the motivation behind these choices.
In particular, the first decision seems to necessitate the addition of the discriminative regularizer (equation between ll. 110-111). However such an objective could be naturally incorporated into the model by adding an optional fully observed sequence-level side information variable y^{(i)} which is generated from z2.
For the variational lower bound results, I would suggest adding a comparison to baseline models such as VRNN (Chung et al. 2015) or SRNN (Fraccaro et al. 2016).
The disentanglement properties of the proposed FHVAE seem to be due to the discriminative objective, since the speaker verification error rate drops as alpha increases. However, since in TIMIT the sequences vary primarily on speaker identity (as mentioned in the paper), the discriminative objective is roughly equivalent to a supervised loss in which the goal is to predict speaker identity. In this case, the disentanglement is no longer unsupervised as originally claimed. Adding a speaker verification experiment on a dataset where the sequences vary less based on speaker identity would help clarify the role of the discriminative objective in disentangling the latent representation.
Clarity
The paper is generally well-written and structured clearly. The notation could be improved in a couple of places. In the inference model (equations between ll. 82-83), I would suggest adding a frame superscript to clarify that inference is occurring within each frame, e.g. q_{\phi}(z_2^{(n)} | x^{(n)}) and q_{\phi}(z_1^{(n)} | x^{(n)}, z_2^{(n)}). In addition, in Section 3 it was not immediately clear that a frame is defined to itself be a sub-sequence.
Originality
The paper is missing a related work section and also does not cite several related works, particularly regarding RNN variants with latent variables (Fraccaro et al. 2016; Chung et al. 2017), hierarchical probabilistic generative models (Johnson et al. 2016; Edwards & Storkey 2017) and disentanglement in generative models (Higgins et al. 2017). The proposed graphical model is similar to that of Edwards & Storkey (2017), though the frame-level Seq2Seq makes the proposed method sufficiently original. The study of disentanglement for sequential data is also fairly novel.
Significance
Unsupervised discovery of disentangled latent representations is an important problem. There has been some prior work in learning disentangled representations for image data but to my knowledge not for sequential data. However the proposed discriminative objective sufficiently resembles supervised identity prediction that it is not clear that the proposed method actually addresses the problem.
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." NIPS 2016.
Chung, Junyoung, et al. "A recurrent latent variable model for sequential data." NIPS 2015.
Chung, Junyoung, Sungjin Ahn, and Yoshua Bengio. "Hierarchical multiscale recurrent neural networks." ICLR 2017.
Edwards, Harrison, and Amos Storkey. "Towards a neural statistician." ICLR 2017.
Fraccaro, Marco, et al. "Sequential neural models with stochastic layers." NIPS 2016.
Higgins, Irina, et al. "beta-VAE: Learning basic visual concepts with a constrained variational framework." ICLR 2017.
Johnson, Matthew, et al. "Composing graphical models with neural networks for structured representations and fast inference." NIPS 2016.
Kulkarni, Tejas D., et al. "Deep convolutional inverse graphics network." NIPS 2015.
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**Edit:** I have read the authors' rebuttal. The clarification was helpful regarding the importance of the discriminative regularizer in achieving disentanglement. I would recommend emphasizing in the paper (1) the presence of multiple utterances per speaker and (2) the speaker verification results are still good when alpha = 0. I am updating my rating of this paper from 4 to 6.