Generative Restricted Kernel Machines
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 representation. Experiments demonstrate the potential of the framework through qualitative evaluation of generated samples.
This is a very good paper, building on the idea of Restricted Kernel Machines (drawing a nice parallel between Restricted Bolzman Machines and tools available in the Kernel modelling literature). In this manuscript, the author(s) extend the work to a generative model setting to achieve multi-view generation -- a generative model that can explain correlated variables from a common subspace. The manuscript is well-written and easy to follow and the algorithmic details are clear. Image generation is illustrated on standard datasets (MNIST / CIFAR / CelebA). While the framework and learning algorithm are good, and novel extensions to what appears to be previous work of the authors, I am less persuaded by the empirical work. Latent variable-based generative modes such as this (and this is motivated in the introduction to the paper) should be judged on if they can extract anything useful about the problem domain in the latent representations that we can interpret. This is not the case here -- the results presented are examples of images that the models can generate. No critical appraisal is given about when the models might fail or when one ought to resort to this approach and not a sample from the plethora of variants of VAE we read about. What have we learnt about images / hand-written characters / faces of popular people from a study like this? From the above empirical results point of view, I do not think this manuscript is ready for publication, despite what I see as the elegance of the framework.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Generative Restricted Kernel Machines
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 representation. Experiments demonstrate the potential of the framework through qualitative evaluation of generated samples.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This is a very good paper, building on the idea of Restricted Kernel Machines (drawing a nice parallel between Restricted Bolzman Machines and tools available in the Kernel modelling literature). In this manuscript, the author(s) extend the work to a generative model setting to achieve multi-view generation -- a generative model that can explain correlated variables from a common subspace. The manuscript is well-written and easy to follow and the algorithmic details are clear. Image generation is illustrated on standard datasets (MNIST / CIFAR / CelebA). While the framework and learning algorithm are good, and novel extensions to what appears to be previous work of the authors, I am less persuaded by the empirical work. Latent variable-based generative modes such as this (and this is motivated in the introduction to the paper) should be judged on if they can extract anything useful about the problem domain in the latent representations that we can interpret. This is not the case here -- the results presented are examples of images that the models can generate. No critical appraisal is given about when the models might fail or when one ought to resort to this approach and not a sample from the plethora of variants of VAE we read about. What have we learnt about images / hand-written characters / faces of popular people from a study like this? From the above empirical results point of view, I do not think this manuscript is ready for publication, despite what I see as the elegance of the framework.
assistant
This is a very good paper, building on the idea of Restricted Kernel Machines (drawing a nice parallel between Restricted Bolzman Machines and tools available in the Kernel modelling literature). In this manuscript, the author(s) extend the work to a generative model setting to achieve multi-view generation -- a generative model that can explain correlated variables from a common subspace. The manuscript is well-written and easy to follow and the algorithmic details are clear. Image generation is illustrated on standard datasets (MNIST / CIFAR / CelebA). While the framework and learning algorithm are good, and novel extensions to what appears to be previous work of the authors, I am less persuaded by the empirical work. Latent variable-based generative modes such as this (and this is motivated in the introduction to the paper) should be judged on if they can extract anything useful about the problem domain in the latent representations that we can interpret. This is not the case here -- the results presented are examples of images that the models can generate. No critical appraisal is given about when the models might fail or when one ought to resort to this approach and not a sample from the plethora of variants of VAE we read about. What have we learnt about images / hand-written characters / faces of popular people from a study like this? From the above empirical results point of view, I do not think this manuscript is ready for publication, despite what I see as the elegance of the framework.