Learning Disentangled Joint Continuous and Discrete Representations
We present a framework for learning disentangled and interpretable jointly continuous and discrete representations in an unsupervised manner. By augmenting the continuous latent distribution of variational autoencoders with a relaxed discrete distribution and controlling the amount of information encoded in each latent unit, we show how continuous and categorical factors of variation can be discovered automatically from data. Experiments show that the framework disentangles continuous and discrete generative factors on various datasets and outperforms current disentangling methods when a discrete generative factor is prominent.
This paper presents a new framework (JointVAE) for learning disentangled representations.The particular problem that this paper focuses on is the  separating discrete and continuous factors of variation in a fully unsupervised manner. Most existing approaches for learning disentangled representations a prior in the form of a Gaussian with diagonal covariance, which is not suitable for capturing categorical features. In the proposed approach a Gaussian distribution represents the continuous features (such as slant and thickness in MNIST) and a Gumbel-Softmax  represents categorical features such (as digit-type). Inducing disentanglement is done by penalizing the KL term between the inference distribution and the prior in a controlled capacity manner as proposed by Burgess et al. (2017) which the upper bound on the mutual information between ‘x’ and ‘z’ gradually increases during training. The results shows successful disentanglement on various datasets including MNIST, Fashion-MNIST, and CelebA.   This is a well-written paper. The authors do a good job of analyzing both the strengths and weaknesses of their proposed method for disentangling discrete features in different datasets. I think that the main of the paper lies in the relatively thorough experimentation. I thought the results in Figure 6 were particularly interesting in that they suggest that there is an ordering in features in terms of mutual information between data and latent variable (for which the KL is an upper bound), where higher mutual information features appear first as the capacity is increased. I also appreciate the explicit discussion of the robust of the degree of disentanglement across restarts, as well as the sensitivity to hyperparameters. Given the difficulties observed in Figure 4 in distinguishing between similar digits (such as 5s and 8s), it would be interesting to see results for this method on a dataset like dSprites, where the shapes are very similar in pixel space. The inferred chair rotations in Figure 7 are also a nice illustration of the ability of the method to generalize to the test set.   The main thing that this paper lacks is a more quantitative evaluation. A number of recent papers have proposed metrics for evaluating disentangled representations. In addition the metrics proposed by Kim & Mnih (2018) and Chen et al. (2018), the work by Eastwood & Williams (2017) [1] is relevant in this context. All of these metrics presume that we have access to labels for true latent factors, which is not the case for any of the datasets considered in the experimentation. However, it would probably be worth evaluating one or more of these metrics on  a dataset such as dSprites.   A minor criticism is that details the training procedure and network architectures are somewhat scarce in the main text. It would be helpful to briefly describe the architectures and training setup in a bit more detail, and explicitly call out the relevant sections of the supplementary material. In particular, it would be good to list key parameters such as γ and the schedule for the capacities Cz and Cc, e.g., the figure captions. In Figure 6a, please mark the 25k iterations (e.g. with a vertical dashed line) to indicate that this is where the capacity is no longer increased further.   Questions  - How robust is the ordering on features  Figure 6, given the noted variability across restarts in Section 4.3? I would hypothesize that the discrete variable always emerges first (given that this variable is in some sense given a “greater” capacity than individual dimensions in the continuous variables). Is the ordering on the continuous variables always the same? What happens when you keep increasing the capacity beyond 25k iterations. Does the network eventually use all of the dimensions of the latent variables?   - I would also appreciate some discussion of how the hyperparameters in the objective were chosen. In particular, one could imagine that the relative magnitude of Cc and Cz would matter, as well as γ. This means that there are more parameter to tune than in, e.g., a vanilla β-VAE. Can the authors comment on how they chose the reported values, and perhaps discuss the sensitivity to these particular hyperparameters in more detail?  - In Figure 2, what is the range of values over which traversal is performed?  Related Work  In addition to the work by Eastwood & Williams, there are a couple of related references that the authors should probably cite:   - Kumar et. al [2] also proposed the total correlation term along with Kim & Mnih (2018) and Chen et al. (2018).   - A recent paper by Esmaeli et al. [3] employs an objective based on the Total Correlation, related to the one in Kim & Mnih (2018) and Chen et. al (2018) to induce disentangled representations that can incorporate both discrete and continuous variables.   Minor Comments  - As the authors write in the introduction, one of the purported advantages of VAEs over GANs is stability of  training. However, as mentioned by the author, including multiple variables of different types also makes the representation unstable. Given this observation, maybe it is worth qualifying these statements in the introduction.   - I would say that section 3.2 can be eliminated - I think that at this point readers can be presumed to know about the Gumbel-Softmax/Concrete distribution.  - Figure 1 could be optimized to use less whitespace.  - I would recommend to replace instances of (\citet{key}) with \citep{key}.  References   [1] Eastwood, C. & Williams, C. K. I. A Framework for the Quantitative Evaluation of Disentangled Representations. (2018).  [2] Kumar, A., Sattigeri, P. & Balakrishnan, A. Variational inference of disentangled latent concepts from unlabeled observations. arXiv preprint arXiv:1711.00848 (2017).  [3] Esmaeili, B. et al. Structured Disentangled Representations. arXiv:1804.02086 [cs, stat] (2018).