Paper:
Learning rich representation from data is an important task for deep generative models such as variational auto-encoder (VAE). However, by extracting high-level abstractions in the bottom-up inference process, the goal of preserving all factors of variations for top-down generation is compromised. Motivated by the concept of "starting small", we present a strategy to progressively learn independent hierarchical representations from high-to low-levels of abstractions. The model starts with learning the most abstract representation, and then progressively grow the network architecture to introduce new representations at different levels of abstraction. We quantitatively demonstrate the ability of the presented model to improve disentanglement in comparison to existing works on two benchmark data sets using three disentanglement metrics, including a new metric we proposed to complement the previously-presented metric of mutual information gap. We further present both qualitative and quantitative evidence on how the progression of learning improves disentangling of hierarchical representations. By drawing on the respective advantage of hierarchical representation learning and progressive learning, this is to our knowledge the first attempt to improve disentanglement by progressively growing the capacity of VAE to learn hierarchical representations.

Review:
This paper proposed a method for training Variational Ladder Autoencoder (VLAE) using a progressive learning strategy. In comparison to the generative model using a progressive learning strategy, the proposed method focuses not only on the image generation but also on extracting and disentangling hierarchical representation.
Overall, I think the purpose of this paper should be written clearly. It is not clear whether the purpose is learning the disentangled representation or the hierarchical representation. In my opinion, I think the focus of the proposed method lies in the hierarchical representation through progressive learning, but the experiments are involved more with disentanglement. Furthermore, I believe the authors need to explain the relationship between hierarchical representation and disentangled representation. In particular, it is not clear why learning hierarchical representation is helpful for disentangled representations.
The qualitative experiments are not convincing since the proposed model looks worse in both the reconstruction and hierarchical disentanglement for MNIST dataset than the base model VLAE, as shown in Figure 5 in [1]. Regarding the metric used in the experiments, the authors mention that the proposed disentanglement metric MIG-sup is what they first developed for one-to-one property, but it seems that it was already proposed in [2]. In addition, the proposed metric requires ground truth for the generative factors, so its usage is limited and not practical.
I think this work is similar to [3] in that both learn disentangled representations by progressively increasing the capacity of the model. I think the authors need to discuss about this work.
Ablation studies should be presented to verify the individual effects of the progressive learning method and implementation strategies on performance, respectively.
In Figures 2 and 3, the performance gap in the reconstruction error of the proposed method is greater than the base model when beta changes from 20 to 30. Therefore, it is necessary to show if it is robust against the hyperparameter beta. 
There is no definition of v_k in Equation (12), so it is difficult to understand the proposed metric clearly.
In summary, I do not think the paper is ready for publication. 
[1] Learning Hierarchical Features from Generative Models, Zhao et al., ICML 2017
[2] A Framework for the Quantitative Evaluation of Disentangled Representations, Eastwood et al., ICLR 2018
[3] Understanding disentangling in beta-VAE, Burgess et al., NIPS 2017 Workshop on Learning Disentangled Representations
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After rebuttal:
Thanks for the revision of the paper and the additional experiments.
The authors' comments and further experiments address most of my concerns. In particular, new experiments show that pro-VLAE performs quantitatively and qualitatively better than VLAE. Also, Figure 10 and the result of the information flow experiment using MNIST show that the first layer learns the intended representations properly.
I appreciate the authors’ efforts put into the rebuttal, and the results of additional experiments are reasonably good. Therefore, I increase my final score to 6: Weak Accept.