Semantic Interpolation in Implicit Models
In implicit models, one often interpolates between sampled points in latent space. As we show in this paper, care needs to be taken to match-up the distributional assumptions on code vectors with the geometry of the interpolating paths. Otherwise, typical assumptions about the quality and semantics of in-between points may not be justified. Based on our analysis we propose to modify the prior code distribution to put significantly more probability mass closer to the origin. As a result, linear interpolation paths are not only shortest paths, but they are also guaranteed to pass through high-density regions, irrespective of the dimensionality of the latent space. Experiments on standard benchmark image datasets demonstrate clear visual improvements in the quality of the generated samples and exhibit more meaningful interpolation paths.
 The authors propose the use of a gamma prior as the distribution over the latent representation space in GANs. The motivation behind it is that in GANs interpolating between sampled points is common in the process of generating examples but the use of a normal prior results in samples that fall in low probability mass regions. The use of the proposed gamma distribution, as a simple alternative, overcomes this problem. In general, the proposed work is very interesting and the idea is neat. The paper is well presented and I want to underline the importance of this. The authors did a very good job presenting the problem, motivation and solution in a coherent fashion and easy to follow. The work itself is interesting and can provide useful alternatives for the distribution over the latent space.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Semantic Interpolation in Implicit Models
In implicit models, one often interpolates between sampled points in latent space. As we show in this paper, care needs to be taken to match-up the distributional assumptions on code vectors with the geometry of the interpolating paths. Otherwise, typical assumptions about the quality and semantics of in-between points may not be justified. Based on our analysis we propose to modify the prior code distribution to put significantly more probability mass closer to the origin. As a result, linear interpolation paths are not only shortest paths, but they are also guaranteed to pass through high-density regions, irrespective of the dimensionality of the latent space. Experiments on standard benchmark image datasets demonstrate clear visual improvements in the quality of the generated samples and exhibit more meaningful interpolation paths.
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>



 The authors propose the use of a gamma prior as the distribution over the latent representation space in GANs. The motivation behind it is that in GANs interpolating between sampled points is common in the process of generating examples but the use of a normal prior results in samples that fall in low probability mass regions. The use of the proposed gamma distribution, as a simple alternative, overcomes this problem. In general, the proposed work is very interesting and the idea is neat. The paper is well presented and I want to underline the importance of this. The authors did a very good job presenting the problem, motivation and solution in a coherent fashion and easy to follow. The work itself is interesting and can provide useful alternatives for the distribution over the latent space.
assistant
 The authors propose the use of a gamma prior as the distribution over the latent representation space in GANs. The motivation behind it is that in GANs interpolating between sampled points is common in the process of generating examples but the use of a normal prior results in samples that fall in low probability mass regions. The use of the proposed gamma distribution, as a simple alternative, overcomes this problem. In general, the proposed work is very interesting and the idea is neat. The paper is well presented and I want to underline the importance of this. The authors did a very good job presenting the problem, motivation and solution in a coherent fashion and easy to follow. The work itself is interesting and can provide useful alternatives for the distribution over the latent space.