Coverage and Quality Driven Training of Generative Image Models
Generative modeling of natural images has been extensively studied in recent years, yielding remarkable progress. Current state-of-the-art methods are either based on maximum likelihood estimation or adversarial training. Both methods have their own drawbacks, which are complementary in nature. The first leads to over-generalization as the maximum likelihood criterion encourages models to cover the support of the training data by heavily penalizing small masses assigned to training data. Simplifying assumptions in such models limits their capacity and makes them spill mass on unrealistic samples. The second leads to mode-dropping since adversarial training encourages high quality samples from the model, but only indirectly enforces diversity among the samples. To overcome these drawbacks we make two contributions. First, we propose a model that extends variational autoencoders by using deterministic invertible transformation layers to map samples from the decoder to the image space. This induces correlations among the pixels given the latent variables, improving over factorial decoders commonly used in variational autoencoders. Second, we propose a unified training approach that leverages coverage and quality based criteria. Our models obtain likelihood scores competitive with state-of-the-art likelihood-based models, while achieving sample quality typical of adversarially trained networks.
 The paper presents the use of an invertible transformtion layer in addition to the conventional variational autoencoder to map samples from decoder to image space, and shows it improves over both synthesis quality and diversity. The paper is well motivated, and the main motivation is nicely presented in Fig.1, and the main idea clearly shown in Fig.2 in an easy-to-understand manner. Existing works are properly discussed in the context before and after the main method. Convincing results are presented in the experimental section, with ablation tests in Tables 1-3, quantitative comparison in Table 4, and qualitative visual images in Figs.4-5. I incline to my current score after reading the response and other reviews.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Coverage and Quality Driven Training of Generative Image Models
Generative modeling of natural images has been extensively studied in recent years, yielding remarkable progress. Current state-of-the-art methods are either based on maximum likelihood estimation or adversarial training. Both methods have their own drawbacks, which are complementary in nature. The first leads to over-generalization as the maximum likelihood criterion encourages models to cover the support of the training data by heavily penalizing small masses assigned to training data. Simplifying assumptions in such models limits their capacity and makes them spill mass on unrealistic samples. The second leads to mode-dropping since adversarial training encourages high quality samples from the model, but only indirectly enforces diversity among the samples. To overcome these drawbacks we make two contributions. First, we propose a model that extends variational autoencoders by using deterministic invertible transformation layers to map samples from the decoder to the image space. This induces correlations among the pixels given the latent variables, improving over factorial decoders commonly used in variational autoencoders. Second, we propose a unified training approach that leverages coverage and quality based criteria. Our models obtain likelihood scores competitive with state-of-the-art likelihood-based models, while achieving sample quality typical of adversarially trained networks.
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 the use of an invertible transformtion layer in addition to the conventional variational autoencoder to map samples from decoder to image space, and shows it improves over both synthesis quality and diversity. The paper is well motivated, and the main motivation is nicely presented in Fig.1, and the main idea clearly shown in Fig.2 in an easy-to-understand manner. Existing works are properly discussed in the context before and after the main method. Convincing results are presented in the experimental section, with ablation tests in Tables 1-3, quantitative comparison in Table 4, and qualitative visual images in Figs.4-5. I incline to my current score after reading the response and other reviews.
assistant
 The paper presents the use of an invertible transformtion layer in addition to the conventional variational autoencoder to map samples from decoder to image space, and shows it improves over both synthesis quality and diversity. The paper is well motivated, and the main motivation is nicely presented in Fig.1, and the main idea clearly shown in Fig.2 in an easy-to-understand manner. Existing works are properly discussed in the context before and after the main method. Convincing results are presented in the experimental section, with ablation tests in Tables 1-3, quantitative comparison in Table 4, and qualitative visual images in Figs.4-5. I incline to my current score after reading the response and other reviews.