Difference-Seeking Generative Adversarial Network
We propose a novel algorithm, Difference-Seeking Generative Adversarial Network (DSGAN), developed from traditional GAN. DSGAN considers the scenario that the training samples of target distribution, , are difficult to collect. Suppose there are two distributions and such that the density of the target distribution can be the differences between the densities of and . We show how to learn the target distribution only via samples from and (relatively easy to obtain). DSGAN has the flexibility to produce samples from various target distributions (e.g. the out-of-distribution). Two key applications, semi-supervised learning and adversarial training, are taken as examples to validate the effectiveness of DSGAN. We also provide theoretical analyses about the convergence of DSGAN.
 The paper presents DS-GAN, which aims to learn the difference between any two distributions whose samples are difficult or impossible to collect. To this end they simply model the target distribution such that adding it to one of the distribution results in another, and propose a min-max objective based on it. To show the effectiveness of the proposed DS-GAN, the authors validate it on semi-supervised learning and adversarial training tasks, on which it performs reasonably well in generating the difference between the two distributions. Pros - The idea of learning the difference between two distributions is novel to my knowledge. Similar ideas have been explored in prior work such as [Li et al. 17] but are not doing exactly what the authors try to do. - The proposed method works reasonably well on semi-supervised learning and adversarial learning tasks, and thus it seems practically useful. Cons - The proposed model is quite straightforward in its formulation, and since the paper is not addressing the importance of, or any challenges with the problem they are trying to solve, the contribution of this work appears minor. - The authors list theoretical results as contributions, but they are rather straightforward replacement of the p_d and p_g terms in the original theorems on optimality in [Goodfellow et al. 14] with the target distributions in this paper, that has nothing to do with what the authors claim in this paper. Thus they add nothing to the value of the paper. - The motivation is very unclear when reading the introduction section, and Figure 1 does not do a good job of providing it. - The experimental validation is lacking in many aspects. I think the main results should show that the difference-seeking GAN can learn distributional differences but the authors jump straight to the applications. Also, the current experimental section simply reports performances on the two tasks, without much analysis showing why it works well and how it works differently from other models. In sum, the idea seems nice and interesting but the model is straightforward and the current results are very weak in analysis in order to make a good paper. I would recommend the authors to perform further analysis of the model either theoretically or experimentally.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Difference-Seeking Generative Adversarial Network
We propose a novel algorithm, Difference-Seeking Generative Adversarial Network (DSGAN), developed from traditional GAN. DSGAN considers the scenario that the training samples of target distribution, , are difficult to collect. Suppose there are two distributions and such that the density of the target distribution can be the differences between the densities of and . We show how to learn the target distribution only via samples from and (relatively easy to obtain). DSGAN has the flexibility to produce samples from various target distributions (e.g. the out-of-distribution). Two key applications, semi-supervised learning and adversarial training, are taken as examples to validate the effectiveness of DSGAN. We also provide theoretical analyses about the convergence of DSGAN.
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 paper presents DS-GAN, which aims to learn the difference between any two distributions whose samples are difficult or impossible to collect. To this end they simply model the target distribution such that adding it to one of the distribution results in another, and propose a min-max objective based on it. To show the effectiveness of the proposed DS-GAN, the authors validate it on semi-supervised learning and adversarial training tasks, on which it performs reasonably well in generating the difference between the two distributions. Pros - The idea of learning the difference between two distributions is novel to my knowledge. Similar ideas have been explored in prior work such as [Li et al. 17] but are not doing exactly what the authors try to do. - The proposed method works reasonably well on semi-supervised learning and adversarial learning tasks, and thus it seems practically useful. Cons - The proposed model is quite straightforward in its formulation, and since the paper is not addressing the importance of, or any challenges with the problem they are trying to solve, the contribution of this work appears minor. - The authors list theoretical results as contributions, but they are rather straightforward replacement of the p_d and p_g terms in the original theorems on optimality in [Goodfellow et al. 14] with the target distributions in this paper, that has nothing to do with what the authors claim in this paper. Thus they add nothing to the value of the paper. - The motivation is very unclear when reading the introduction section, and Figure 1 does not do a good job of providing it. - The experimental validation is lacking in many aspects. I think the main results should show that the difference-seeking GAN can learn distributional differences but the authors jump straight to the applications. Also, the current experimental section simply reports performances on the two tasks, without much analysis showing why it works well and how it works differently from other models. In sum, the idea seems nice and interesting but the model is straightforward and the current results are very weak in analysis in order to make a good paper. I would recommend the authors to perform further analysis of the model either theoretically or experimentally.
assistant
 The paper presents DS-GAN, which aims to learn the difference between any two distributions whose samples are difficult or impossible to collect. To this end they simply model the target distribution such that adding it to one of the distribution results in another, and propose a min-max objective based on it. To show the effectiveness of the proposed DS-GAN, the authors validate it on semi-supervised learning and adversarial training tasks, on which it performs reasonably well in generating the difference between the two distributions. Pros - The idea of learning the difference between two distributions is novel to my knowledge. Similar ideas have been explored in prior work such as [Li et al. 17] but are not doing exactly what the authors try to do. - The proposed method works reasonably well on semi-supervised learning and adversarial learning tasks, and thus it seems practically useful. Cons - The proposed model is quite straightforward in its formulation, and since the paper is not addressing the importance of, or any challenges with the problem they are trying to solve, the contribution of this work appears minor. - The authors list theoretical results as contributions, but they are rather straightforward replacement of the p_d and p_g terms in the original theorems on optimality in [Goodfellow et al. 14] with the target distributions in this paper, that has nothing to do with what the authors claim in this paper. Thus they add nothing to the value of the paper. - The motivation is very unclear when reading the introduction section, and Figure 1 does not do a good job of providing it. - The experimental validation is lacking in many aspects. I think the main results should show that the difference-seeking GAN can learn distributional differences but the authors jump straight to the applications. Also, the current experimental section simply reports performances on the two tasks, without much analysis showing why it works well and how it works differently from other models. In sum, the idea seems nice and interesting but the model is straightforward and the current results are very weak in analysis in order to make a good paper. I would recommend the authors to perform further analysis of the model either theoretically or experimentally.