Unsupervised Adversarial Image Reconstruction
We address the problem of recovering an underlying signal from lossy, inaccurate observations in an unsupervised setting. Typically, we consider situations where there is little to no background knowledge on the structure of the underlying signal, no access to signal-measurement pairs, nor even unpaired signal-measurement data. The only available information is provided by the observations and the measurement process statistics. We cast the problem as finding the 	extit{maximum a posteriori} estimate of the signal given each measurement, and propose a general framework for the reconstruction problem. We use a formulation of generative adversarial networks, where the generator takes as input a corrupted observation in order to produce realistic reconstructions, and add a penalty term tying the reconstruction to the associated observation. We evaluate our reconstructions on several image datasets with different types of corruptions. The proposed approach yields better results than alternative baselines, and comparable performance with model variants trained with additional supervision.
 This paper presents a method to reconstruct images using only noisy measurements. This problem is practically interesting, since the noiseless signal may be unavailable in many applications. The approach combines ideas from recent development in compressed sensing and GANs. However, the model’s presentation is confusing, and many important details of the experiments are missing. Pros: * The problem is interesting and important * The combination of compressed sensing and GANs for image reconstruction is novel Cons: * The model structure is unclear: for example, what is the role of the variable .theta? Section 2.1 says it is known, but the algorithm samples from its prior(?). Since there is no further explanation with respect to the experiments, I am not sure how the values of .theta or its distributions were determined. Although .theta is formally similar to the .theta parameters of the measurement function in ambientGANs, this interpretation is at odds with the example given in the paper (below eq.1, saying .theta can be positions or sizes). * A few important details of the model are missing. For example, what is the exact structure of the measurement function F? * The baseline models are a bit confusing. More detail about unpaired vs paired supervision would also be helpful for understanding how these baseline models use the additional information. * Although the paper mentioned parameters are obtained from cross-validation, it would still be helpful to describe a few important ones (e.g., neural network size, weight .lambda) for comparison with other models.The experiments on only CelebA dataset are too limited.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Unsupervised Adversarial Image Reconstruction
We address the problem of recovering an underlying signal from lossy, inaccurate observations in an unsupervised setting. Typically, we consider situations where there is little to no background knowledge on the structure of the underlying signal, no access to signal-measurement pairs, nor even unpaired signal-measurement data. The only available information is provided by the observations and the measurement process statistics. We cast the problem as finding the 	extit{maximum a posteriori} estimate of the signal given each measurement, and propose a general framework for the reconstruction problem. We use a formulation of generative adversarial networks, where the generator takes as input a corrupted observation in order to produce realistic reconstructions, and add a penalty term tying the reconstruction to the associated observation. We evaluate our reconstructions on several image datasets with different types of corruptions. The proposed approach yields better results than alternative baselines, and comparable performance with model variants trained with additional supervision.
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>



 This paper presents a method to reconstruct images using only noisy measurements. This problem is practically interesting, since the noiseless signal may be unavailable in many applications. The approach combines ideas from recent development in compressed sensing and GANs. However, the model’s presentation is confusing, and many important details of the experiments are missing. Pros: * The problem is interesting and important * The combination of compressed sensing and GANs for image reconstruction is novel Cons: * The model structure is unclear: for example, what is the role of the variable .theta? Section 2.1 says it is known, but the algorithm samples from its prior(?). Since there is no further explanation with respect to the experiments, I am not sure how the values of .theta or its distributions were determined. Although .theta is formally similar to the .theta parameters of the measurement function in ambientGANs, this interpretation is at odds with the example given in the paper (below eq.1, saying .theta can be positions or sizes). * A few important details of the model are missing. For example, what is the exact structure of the measurement function F? * The baseline models are a bit confusing. More detail about unpaired vs paired supervision would also be helpful for understanding how these baseline models use the additional information. * Although the paper mentioned parameters are obtained from cross-validation, it would still be helpful to describe a few important ones (e.g., neural network size, weight .lambda) for comparison with other models.The experiments on only CelebA dataset are too limited.
assistant
 This paper presents a method to reconstruct images using only noisy measurements. This problem is practically interesting, since the noiseless signal may be unavailable in many applications. The approach combines ideas from recent development in compressed sensing and GANs. However, the model’s presentation is confusing, and many important details of the experiments are missing. Pros: * The problem is interesting and important * The combination of compressed sensing and GANs for image reconstruction is novel Cons: * The model structure is unclear: for example, what is the role of the variable .theta? Section 2.1 says it is known, but the algorithm samples from its prior(?). Since there is no further explanation with respect to the experiments, I am not sure how the values of .theta or its distributions were determined. Although .theta is formally similar to the .theta parameters of the measurement function in ambientGANs, this interpretation is at odds with the example given in the paper (below eq.1, saying .theta can be positions or sizes). * A few important details of the model are missing. For example, what is the exact structure of the measurement function F? * The baseline models are a bit confusing. More detail about unpaired vs paired supervision would also be helpful for understanding how these baseline models use the additional information. * Although the paper mentioned parameters are obtained from cross-validation, it would still be helpful to describe a few important ones (e.g., neural network size, weight .lambda) for comparison with other models.The experiments on only CelebA dataset are too limited.