Density estimation using Real NVP
Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. We extend the space of such models using real-valued non-volume preserving (real NVP) transformations, a set of powerful invertible and learnable transformations, resulting in an unsupervised learning algorithm with exact log-likelihood computation, exact sampling, exact inference of latent variables, and an interpretable latent space. We demonstrate its ability to model natural images on four datasets through sampling, log-likelihood evaluation and latent variable manipulations.
This paper presents a clever way of training a generative model which allows for exact inference, sampling and log likelihood evaluation. The main idea here is to make the Jacobian that comes when using the change of variables formula (from data to latents) triangular - this makes the determinant easy to calculate and hence learning possible. The paper nicely presents this core idea and a way to achieve this - by choosing special *routings* between the latents and data such that part of the transformation is identity and part some complex function of the input (a deep net, for example) the resulting Jacobian has a tractable structure. This routing can be cascaded to achieve even more complex transformation. On the experimental side, the model is trained on several datasets and the results are quite convincing, both in sample quality and quantitive measures. I would be very happy to see if this model is useful with other types of tasks and if the resulting latent representation help with classification or inference such as image restoration. In summary - the paper is nicely written, results are quite good and the model is interesting - I*m happy to recommend acceptance.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Density estimation using Real NVP
Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. We extend the space of such models using real-valued non-volume preserving (real NVP) transformations, a set of powerful invertible and learnable transformations, resulting in an unsupervised learning algorithm with exact log-likelihood computation, exact sampling, exact inference of latent variables, and an interpretable latent space. We demonstrate its ability to model natural images on four datasets through sampling, log-likelihood evaluation and latent variable manipulations.
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 paper presents a clever way of training a generative model which allows for exact inference, sampling and log likelihood evaluation. The main idea here is to make the Jacobian that comes when using the change of variables formula (from data to latents) triangular - this makes the determinant easy to calculate and hence learning possible. The paper nicely presents this core idea and a way to achieve this - by choosing special *routings* between the latents and data such that part of the transformation is identity and part some complex function of the input (a deep net, for example) the resulting Jacobian has a tractable structure. This routing can be cascaded to achieve even more complex transformation. On the experimental side, the model is trained on several datasets and the results are quite convincing, both in sample quality and quantitive measures. I would be very happy to see if this model is useful with other types of tasks and if the resulting latent representation help with classification or inference such as image restoration. In summary - the paper is nicely written, results are quite good and the model is interesting - I*m happy to recommend acceptance.
assistant
This paper presents a clever way of training a generative model which allows for exact inference, sampling and log likelihood evaluation. The main idea here is to make the Jacobian that comes when using the change of variables formula (from data to latents) triangular - this makes the determinant easy to calculate and hence learning possible. The paper nicely presents this core idea and a way to achieve this - by choosing special *routings* between the latents and data such that part of the transformation is identity and part some complex function of the input (a deep net, for example) the resulting Jacobian has a tractable structure. This routing can be cascaded to achieve even more complex transformation. On the experimental side, the model is trained on several datasets and the results are quite convincing, both in sample quality and quantitive measures. I would be very happy to see if this model is useful with other types of tasks and if the resulting latent representation help with classification or inference such as image restoration. In summary - the paper is nicely written, results are quite good and the model is interesting - I*m happy to recommend acceptance.