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.
 - Summary: This paper considers the problem of learning a GAN to capture a target distribution p_t with only very few training samples from p_t available. - Good An interesting problem formulation. The proposed approach is not new, but seems to be a sensible and simple solution to the problem formulated in this paper. I would see the contributions of the paper: (1) an interesting problem formulation on how to learn p_t (with a few assumptions) (2) a sensible adaptation of GANs on this problem (with minor modifications to GANs which have been observed/adopted in many GAN literatures in the last two years) The training appaoches/tricks are rather straightforward and not new as well. - Suggestions The main problem of this paper is that it does not provide sufficient results on any real applications that can support its problem&model formulations. For example, in which scenarios would the users of the model need to train a GAN to mimic a target distribution p_t which is a difference of another two distributions (with examples available there but unavailable in p_t)? It would be good to show significant results on real applications to show the problem and the method useful. Two applications on semi-supervised classification and adversarial training are discussed. While both seem to be very artificial IMO if considering the used dataset and designed experiments. The results are also not convincing even for the shown two experiments compared to baselines. No related works on addressing the similar problems have been discussed nor compared in experiments. - Theoretical results: While the authors claim new theoretical results, in fact, I didn*t see any contributions here as the theories developed in section 3 are mostly rather straightforward. There have been some similar theories being developed in previous papers where a component in GAN exhibits mixture-modeled forms, such as in TripleGan (Li et al. NIPS*17). So I would not recommend the authors to claim contributions here. - Writing: The paper does not seem to be polished. It may not be necessary to exceed 8 pages as many spaces in this paper could be easily squeezed (apparently). The organization could be better; Some parts are vague and difficult to understand; the writing could be improved to be more clearly demonstrate the contributions of this paper.
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>



 - Summary: This paper considers the problem of learning a GAN to capture a target distribution p_t with only very few training samples from p_t available. - Good An interesting problem formulation. The proposed approach is not new, but seems to be a sensible and simple solution to the problem formulated in this paper. I would see the contributions of the paper: (1) an interesting problem formulation on how to learn p_t (with a few assumptions) (2) a sensible adaptation of GANs on this problem (with minor modifications to GANs which have been observed/adopted in many GAN literatures in the last two years) The training appaoches/tricks are rather straightforward and not new as well. - Suggestions The main problem of this paper is that it does not provide sufficient results on any real applications that can support its problem&model formulations. For example, in which scenarios would the users of the model need to train a GAN to mimic a target distribution p_t which is a difference of another two distributions (with examples available there but unavailable in p_t)? It would be good to show significant results on real applications to show the problem and the method useful. Two applications on semi-supervised classification and adversarial training are discussed. While both seem to be very artificial IMO if considering the used dataset and designed experiments. The results are also not convincing even for the shown two experiments compared to baselines. No related works on addressing the similar problems have been discussed nor compared in experiments. - Theoretical results: While the authors claim new theoretical results, in fact, I didn*t see any contributions here as the theories developed in section 3 are mostly rather straightforward. There have been some similar theories being developed in previous papers where a component in GAN exhibits mixture-modeled forms, such as in TripleGan (Li et al. NIPS*17). So I would not recommend the authors to claim contributions here. - Writing: The paper does not seem to be polished. It may not be necessary to exceed 8 pages as many spaces in this paper could be easily squeezed (apparently). The organization could be better; Some parts are vague and difficult to understand; the writing could be improved to be more clearly demonstrate the contributions of this paper.
assistant
 - Summary: This paper considers the problem of learning a GAN to capture a target distribution p_t with only very few training samples from p_t available. - Good An interesting problem formulation. The proposed approach is not new, but seems to be a sensible and simple solution to the problem formulated in this paper. I would see the contributions of the paper: (1) an interesting problem formulation on how to learn p_t (with a few assumptions) (2) a sensible adaptation of GANs on this problem (with minor modifications to GANs which have been observed/adopted in many GAN literatures in the last two years) The training appaoches/tricks are rather straightforward and not new as well. - Suggestions The main problem of this paper is that it does not provide sufficient results on any real applications that can support its problem&model formulations. For example, in which scenarios would the users of the model need to train a GAN to mimic a target distribution p_t which is a difference of another two distributions (with examples available there but unavailable in p_t)? It would be good to show significant results on real applications to show the problem and the method useful. Two applications on semi-supervised classification and adversarial training are discussed. While both seem to be very artificial IMO if considering the used dataset and designed experiments. The results are also not convincing even for the shown two experiments compared to baselines. No related works on addressing the similar problems have been discussed nor compared in experiments. - Theoretical results: While the authors claim new theoretical results, in fact, I didn*t see any contributions here as the theories developed in section 3 are mostly rather straightforward. There have been some similar theories being developed in previous papers where a component in GAN exhibits mixture-modeled forms, such as in TripleGan (Li et al. NIPS*17). So I would not recommend the authors to claim contributions here. - Writing: The paper does not seem to be polished. It may not be necessary to exceed 8 pages as many spaces in this paper could be easily squeezed (apparently). The organization could be better; Some parts are vague and difficult to understand; the writing could be improved to be more clearly demonstrate the contributions of this paper.