Paper:
We introduce a novel framework for generative models based on Restricted Kernel Machines (RKMs) with multi-view generation and uncorrelated feature learning capabilities, called Gen-RKM. To incorporate multi-view generation, this mechanism uses a shared representation of data from various views. The mechanism is flexible to incorporate both kernel-based, (deep) neural network and Convolutional based models within the same setting. To update the parameters of the network, we propose a novel training procedure which jointly learns the features and shared subspace representation. The latent variables are given by the eigendecomposition of the kernel matrix, where the mutual orthogonality of eigenvectors represents uncorrelated features. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of generated samples on various standard datasets.

Review:
This paper presents a model and training framework for generating samples based on restricted kernel machines. It is extended to multi-view generation and uncorrelated feature representation learning. 
- The paper is well-written and well-organized. Notations and claims are clear.
- The idea of a multi-view generation model based on restricted kernel machines is interesting. However, the paper seems to be limited to model definition and algorithm overview without a performance evaluating analysis. 
- The experimental evaluations are not satisfactory. Although it is claimed in the paper that the model is able to generate high quality images, it is very hard to be confirmed with these experiments. There is no concrete attempt at comparing the performance of the model to the other used methodologies. Generating high quality images with multiple views is an interesting problem, and there are good works in the field addressing the issues. To name a few:
Zhu, Z., Luo, P., Wang, X., Tang, X.: Multi-view perceptron: a deep model for learning face identity and view representations. In: Advances in Neural Information Processing Systems (NIPS). pp. 217–225, (2014)
Kan, M., Shan, S., Chen, X.: Multi-view deep network for cross-view classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4847–4855, (2016) 
Yin, X., Liu, X.: Multi-task convolutional neural network for pose-invariant face recognition. IEEE Transactions on Image Processing (2017) 
Yim, J., Jung, H., Yoo, B., Choi, C., Park, D., Kim, J.: Rotating your face using multitask deep neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 676–684, (2015)
Yu Tian, Xi Peng , Long Zhao, Shaoting Zhang , ,Dimitris N. Metaxas , CR-GAN: Learning Complete Representations for Multi-view Generation, arXiv: 1806.11191, 2018.
There might be differences between these works and the paper, it is common to evaluate the quality of the generation to other models in terms of accuracy or in the classification tasks. Unfortunately, there is no such quantitative analysis in the paper. So the advantages of the proposed model is not very clear since there is not enough quantitative performance analysis. It would be interesting to see complexity analysis to evaluate the computational costs.
Overall, I do not recommend this paper for publication. The experimental results are not satisfactory, and the paper needs improvements in that regard.
** update:
I would like to thank the authors for their comments. However, I still see major issues in the paper unresolved and my review remains the same.