Label super-resolution networks
We present a deep learning-based method for super-resolving coarse (low-resolution) labels assigned to groups of image pixels into pixel-level (high-resolution) labels, given the joint distribution between those low- and high-resolution labels. This method involves a novel loss function that minimizes the distance between a distribution determined by a set of model outputs and the corresponding distribution given by low-resolution labels over the same set of outputs. This setup does not require that the high-resolution classes match the low-resolution classes and can be used in high-resolution semantic segmentation tasks where high-resolution labeled data is not available. Furthermore, our proposed method is able to utilize both data with low-resolution labels and any available high-resolution labels, which we show improves performance compared to a network trained only with the same amount of high-resolution data. We test our proposed algorithm in a challenging land cover mapping task to super-resolve labels at a 30m resolution to a separate set of labels at a 1m resolution. We compare our algorithm with models that are trained on high-resolution data and show that 1) we can achieve similar performance using only low-resolution data; and 2) we can achieve better performance when we incorporate a small amount of high-resolution data in our training. We also test our approach on a medical imaging problem, resolving low-resolution probability maps into high-resolution segmentation of lymphocytes with accuracy equal to that of fully supervised models.
 The authors present a technique to exploit low resolution labels from a space Z to provide weak supervision to a semantic segmentation network which predicts high resolution labels from a different space Y, assuming that a joint distribution of Z, Y is known a-priori. The paper is very well written and easy to follow, the main contribution is clearly and rigorously explained in the technical section. The technical contribution is somehow limited, but it is substantially validated by a very well organized and convincing experimental evaluation. Overall, I have three main points of criticism (detailed in the following), which however aren*t enough to not recommend this paper for acceptance. Main cons: 1) At points, the paper reads more like a technical report about solving specific problems in land cover estimation and lymphocyte segmentation than a machine learning paper. Many paragraphs are devoted to describe the specifics of these two problems and to design methods to overcome them. The main technical contribution of the paper seems to be specifically tailored to solve the particular setup encountered in land cover estimation, i.e. two different sets of labels with different resolution on the same segmentation data, which ties to the next point. 2) One important limitation lies in the fact that the distribution p(c|z) needs to be known a-priori and somehow derived from additional problem-specific knowledge. This is not an issue in the two tasks considered in the paper, but in my opinion it could severely limit the applicability of the proposed approach. I think the paper would benefit from the inclusion of some discussion about how this limitation could be overcome. 3) It*s not very clear to me why the gaussian approximation with the specific mean and variance values defined in eq.4 would be a good approximation for p_net(c_lk|X). Could the authors expand on this?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Label super-resolution networks
We present a deep learning-based method for super-resolving coarse (low-resolution) labels assigned to groups of image pixels into pixel-level (high-resolution) labels, given the joint distribution between those low- and high-resolution labels. This method involves a novel loss function that minimizes the distance between a distribution determined by a set of model outputs and the corresponding distribution given by low-resolution labels over the same set of outputs. This setup does not require that the high-resolution classes match the low-resolution classes and can be used in high-resolution semantic segmentation tasks where high-resolution labeled data is not available. Furthermore, our proposed method is able to utilize both data with low-resolution labels and any available high-resolution labels, which we show improves performance compared to a network trained only with the same amount of high-resolution data. We test our proposed algorithm in a challenging land cover mapping task to super-resolve labels at a 30m resolution to a separate set of labels at a 1m resolution. We compare our algorithm with models that are trained on high-resolution data and show that 1) we can achieve similar performance using only low-resolution data; and 2) we can achieve better performance when we incorporate a small amount of high-resolution data in our training. We also test our approach on a medical imaging problem, resolving low-resolution probability maps into high-resolution segmentation of lymphocytes with accuracy equal to that of fully supervised models.
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 authors present a technique to exploit low resolution labels from a space Z to provide weak supervision to a semantic segmentation network which predicts high resolution labels from a different space Y, assuming that a joint distribution of Z, Y is known a-priori. The paper is very well written and easy to follow, the main contribution is clearly and rigorously explained in the technical section. The technical contribution is somehow limited, but it is substantially validated by a very well organized and convincing experimental evaluation. Overall, I have three main points of criticism (detailed in the following), which however aren*t enough to not recommend this paper for acceptance. Main cons: 1) At points, the paper reads more like a technical report about solving specific problems in land cover estimation and lymphocyte segmentation than a machine learning paper. Many paragraphs are devoted to describe the specifics of these two problems and to design methods to overcome them. The main technical contribution of the paper seems to be specifically tailored to solve the particular setup encountered in land cover estimation, i.e. two different sets of labels with different resolution on the same segmentation data, which ties to the next point. 2) One important limitation lies in the fact that the distribution p(c|z) needs to be known a-priori and somehow derived from additional problem-specific knowledge. This is not an issue in the two tasks considered in the paper, but in my opinion it could severely limit the applicability of the proposed approach. I think the paper would benefit from the inclusion of some discussion about how this limitation could be overcome. 3) It*s not very clear to me why the gaussian approximation with the specific mean and variance values defined in eq.4 would be a good approximation for p_net(c_lk|X). Could the authors expand on this?
assistant
 The authors present a technique to exploit low resolution labels from a space Z to provide weak supervision to a semantic segmentation network which predicts high resolution labels from a different space Y, assuming that a joint distribution of Z, Y is known a-priori. The paper is very well written and easy to follow, the main contribution is clearly and rigorously explained in the technical section. The technical contribution is somehow limited, but it is substantially validated by a very well organized and convincing experimental evaluation. Overall, I have three main points of criticism (detailed in the following), which however aren*t enough to not recommend this paper for acceptance. Main cons: 1) At points, the paper reads more like a technical report about solving specific problems in land cover estimation and lymphocyte segmentation than a machine learning paper. Many paragraphs are devoted to describe the specifics of these two problems and to design methods to overcome them. The main technical contribution of the paper seems to be specifically tailored to solve the particular setup encountered in land cover estimation, i.e. two different sets of labels with different resolution on the same segmentation data, which ties to the next point. 2) One important limitation lies in the fact that the distribution p(c|z) needs to be known a-priori and somehow derived from additional problem-specific knowledge. This is not an issue in the two tasks considered in the paper, but in my opinion it could severely limit the applicability of the proposed approach. I think the paper would benefit from the inclusion of some discussion about how this limitation could be overcome. 3) It*s not very clear to me why the gaussian approximation with the specific mean and variance values defined in eq.4 would be a good approximation for p_net(c_lk|X). Could the authors expand on this?