Linearizing Visual Processes with Deep Generative Models
This work studies the problem of modeling non-linear visual processes by leveraging deep generative architectures for learning linear, Gaussian models of observed sequences. We propose a joint learning framework, combining a multivariate autoregressive model and deep convolutional generative networks. After justification of theoretical assumptions of inearization, we propose an architecture that allows Variational Autoencoders and Generative Adversarial Networks to simultaneously learn the non-linear observation as well as the linear state-transition model from a sequence of observed frames. Finally, we demonstrate our approach on conceptual toy examples and dynamic textures.
 Quick summary: The author investigate if we can learn a linearized state space model using deep generative models to guide and transform non-linear dynamic observations into linear state space processes. A quick analysis of the feasibilty of the model and its relation to existing models is provided. Experimental results include a GAN and a VAE as the generative model on a few datasets. Pros: - The model is interesting and the motivation is quite clear - analysis is quite nice - writing is quite clear and decent Cons: - Extremely lacking experimental validation - there are literally no baseline models, no numbers or any kind of quantitative analysis. The figures show samples from the model with very little explanation or discussion and it*s entirely unclear what we learn from this model. Scientifically speaking, this is not up to par. Bottom line - I think this is a good start for a paper about an interesting model, but I don*t feel that this teaches us anything about what the model learns and how it relates in practice to other models.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Linearizing Visual Processes with Deep Generative Models
This work studies the problem of modeling non-linear visual processes by leveraging deep generative architectures for learning linear, Gaussian models of observed sequences. We propose a joint learning framework, combining a multivariate autoregressive model and deep convolutional generative networks. After justification of theoretical assumptions of inearization, we propose an architecture that allows Variational Autoencoders and Generative Adversarial Networks to simultaneously learn the non-linear observation as well as the linear state-transition model from a sequence of observed frames. Finally, we demonstrate our approach on conceptual toy examples and dynamic textures.
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>



 Quick summary: The author investigate if we can learn a linearized state space model using deep generative models to guide and transform non-linear dynamic observations into linear state space processes. A quick analysis of the feasibilty of the model and its relation to existing models is provided. Experimental results include a GAN and a VAE as the generative model on a few datasets. Pros: - The model is interesting and the motivation is quite clear - analysis is quite nice - writing is quite clear and decent Cons: - Extremely lacking experimental validation - there are literally no baseline models, no numbers or any kind of quantitative analysis. The figures show samples from the model with very little explanation or discussion and it*s entirely unclear what we learn from this model. Scientifically speaking, this is not up to par. Bottom line - I think this is a good start for a paper about an interesting model, but I don*t feel that this teaches us anything about what the model learns and how it relates in practice to other models.
assistant
 Quick summary: The author investigate if we can learn a linearized state space model using deep generative models to guide and transform non-linear dynamic observations into linear state space processes. A quick analysis of the feasibilty of the model and its relation to existing models is provided. Experimental results include a GAN and a VAE as the generative model on a few datasets. Pros: - The model is interesting and the motivation is quite clear - analysis is quite nice - writing is quite clear and decent Cons: - Extremely lacking experimental validation - there are literally no baseline models, no numbers or any kind of quantitative analysis. The figures show samples from the model with very little explanation or discussion and it*s entirely unclear what we learn from this model. Scientifically speaking, this is not up to par. Bottom line - I think this is a good start for a paper about an interesting model, but I don*t feel that this teaches us anything about what the model learns and how it relates in practice to other models.