LDMGAN: Reducing Mode Collapse in GANs with Latent Distribution Matching
Generative Adversarial Networks (GANs) have shown impressive results in modeling distributions over complicated manifolds such as those of natural images. However, GANs often suffer from mode collapse, which means they are prone to characterize only a single or a few modes of the data distribution. In order to address this problem, we propose a novel framework called LDMGAN. We ?rst introduce Latent Distribution Matching (LDM) constraint which regularizes the generator by aligning distribution of generated samples with that of real samples in latent space. To make use of such latent space, we propose a regularized AutoEncoder (AE) that maps the data distribution to prior distribution in encoded space. Extensive experiments on synthetic data and real world datasets show that our proposed framework signi?cantly improves GAN’s stability and diversity.
The manuscript contains typing and grammatical errors. I also think that the presentation is not clear enough. The notation is not clearly defined also. I hope I am not missing it, but the function combination operation is not defined (e.g. f(x) o g(x), to denote f(g(x)), and used a lot. Just under equation 4, you are saying that comparing empirical distributions is difficult, and hence propose an alternative objective. But you could actually compare the two empirical distributions with a sample based divergence measure such maximum mean discrepancy, or Frechet Inception distance. Why don*t you do this? In the algorithms section, I don*t understand why you don*t compare with widely used WGAN-GP model. DCGAN, and VAEGAN are relatively old models. Your only results in Celeb-A are compared against these relatively older models. Also by looking at the provided samples, I think the quality of the results are far away from what is obtained with state of the Generative models currently. Overall, I think the idea is sound and sensible, but I don*t think that results are convincing enough for a conference paper. Furthermore, the writing needs to be improved. minor comments: In Figure 2, I think you should provide the explanations also in the caption to make the concept easier to follow for the reader. In Algorithm 1, I think it would be easier for the reader to follow if you don*t use shorthand notations defined in lines 5, 6, 7, 8.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
LDMGAN: Reducing Mode Collapse in GANs with Latent Distribution Matching
Generative Adversarial Networks (GANs) have shown impressive results in modeling distributions over complicated manifolds such as those of natural images. However, GANs often suffer from mode collapse, which means they are prone to characterize only a single or a few modes of the data distribution. In order to address this problem, we propose a novel framework called LDMGAN. We ?rst introduce Latent Distribution Matching (LDM) constraint which regularizes the generator by aligning distribution of generated samples with that of real samples in latent space. To make use of such latent space, we propose a regularized AutoEncoder (AE) that maps the data distribution to prior distribution in encoded space. Extensive experiments on synthetic data and real world datasets show that our proposed framework signi?cantly improves GAN’s stability and diversity.
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 manuscript contains typing and grammatical errors. I also think that the presentation is not clear enough. The notation is not clearly defined also. I hope I am not missing it, but the function combination operation is not defined (e.g. f(x) o g(x), to denote f(g(x)), and used a lot. Just under equation 4, you are saying that comparing empirical distributions is difficult, and hence propose an alternative objective. But you could actually compare the two empirical distributions with a sample based divergence measure such maximum mean discrepancy, or Frechet Inception distance. Why don*t you do this? In the algorithms section, I don*t understand why you don*t compare with widely used WGAN-GP model. DCGAN, and VAEGAN are relatively old models. Your only results in Celeb-A are compared against these relatively older models. Also by looking at the provided samples, I think the quality of the results are far away from what is obtained with state of the Generative models currently. Overall, I think the idea is sound and sensible, but I don*t think that results are convincing enough for a conference paper. Furthermore, the writing needs to be improved. minor comments: In Figure 2, I think you should provide the explanations also in the caption to make the concept easier to follow for the reader. In Algorithm 1, I think it would be easier for the reader to follow if you don*t use shorthand notations defined in lines 5, 6, 7, 8.
assistant
The manuscript contains typing and grammatical errors. I also think that the presentation is not clear enough. The notation is not clearly defined also. I hope I am not missing it, but the function combination operation is not defined (e.g. f(x) o g(x), to denote f(g(x)), and used a lot. Just under equation 4, you are saying that comparing empirical distributions is difficult, and hence propose an alternative objective. But you could actually compare the two empirical distributions with a sample based divergence measure such maximum mean discrepancy, or Frechet Inception distance. Why don*t you do this? In the algorithms section, I don*t understand why you don*t compare with widely used WGAN-GP model. DCGAN, and VAEGAN are relatively old models. Your only results in Celeb-A are compared against these relatively older models. Also by looking at the provided samples, I think the quality of the results are far away from what is obtained with state of the Generative models currently. Overall, I think the idea is sound and sensible, but I don*t think that results are convincing enough for a conference paper. Furthermore, the writing needs to be improved. minor comments: In Figure 2, I think you should provide the explanations also in the caption to make the concept easier to follow for the reader. In Algorithm 1, I think it would be easier for the reader to follow if you don*t use shorthand notations defined in lines 5, 6, 7, 8.