Paper:
CapsNet (Capsule Network) was first proposed by Sabour et al. (2017) and later another version of CapsNet was proposed by Hinton et al. (2018). CapsNet has been proved effective in modeling spatial features with much fewer parameters. However, the routing procedures (dynamic routing and EM routing) in both papers are not well incorporated into the whole training process, and the optimal number for the routing procedure has to be found manually. We propose Generalized GapsNet (G-CapsNet) to overcome this disadvantages by incorporating the routing procedure into the optimization. We implement two versions of GCapsNet (fully-connected and convolutional) on CAFFE (Jia et al. (2014)) and evaluate them by testing the accuracy on MNIST & CIFAR10, the robustness to white-box & black-box attack, and the generalization ability on GAN-generated synthetic images. We also explore the scalability of G-CapsNet by constructing a relatively deep G-CapsNet. The experiment shows that G-CapsNet has good generalization ability and scalability.

Review:
The paper deals with the idea to generalize the CapsNet architecture from Sabour. Under generalization the authors mean, to define a routing procedure without an iteration parameter.
In general, your paper has a good length, is well explained and good organized. To be honest, I don’t like your writing style. It seems to be a bit too casual and not formal enough for a scientific work. Additionally, note that:
-	You are not staring with Fig. 1 in the introduction…the counting should start by one.
-	AC-GAN is the abbreviation for? 
-	Eq. 2 is outside the page space.
I have several concerns about the contribution. My major concern is that it isn’t new in general. If I break down your method, it is just the basic Dynamic Routing procedure with:
-	the number of iterations defined to be one;
-	trainable initial routing coefficients;
-	no softmax normalization over routing coefficients.
The usage of trainable initial routing coefficients was already mentioned by Sabour. Thus, the only thing which is new in your method is that you skip the normalization and I’m not sure that this has a positive effect on the process. 
Minor concerns/minor mistakes:
1.	You mentioned that the code is public available. Where is the link to a respective repository?
2.	Page 1: “[…] so it makes sense to believe that CapsNet has a better generalization ability.” Compared to what?
3.	Page 2: “The routing iterations is a meta-parameter that needs to be set manually which limits the scalability of CapsNet […].” Why it should limit the scalability? It has no effect on the model size, etc. It’s just a parameter which has to be defined.
4.	Page 3: Are you sure that a linear transformation of a hyper-cube is defined in that way in general?
5.	Page 4: What is T_k?
6.	Page 7: “Hinton et al. (2018) claimed that CapsNets […]” Are you aware that Hinton worked on Matrix Capsules and not on the CapsNet architecture of Sabour?
7.	Page 8: How you can guarantee the convergence of your method? Moreover, the convergence to what?
8.	Could you add some histograms plots of your c_ij values after the training?
9.	Why are your performance values so bad compared to CapsNet and Matrix Capsules?
10.	Could you add to your tables the inference, training times? If you remove the iteration parameter I would assume that your method should be faster, or?
11.	Is the parameter lambda in Eq. 2 the same as in Eq. 6? How you tune that parameter?