Contrastive Multiview Coding
Humans view the world through many sensory channels, e.g., the long-wavelength light channel, viewed by the left eye, or the high-frequency vibrations channel, heard by the right ear. Each view is noisy and incomplete, but important factors, such as physics, geometry, and semantics, tend to be shared between all views (e.g., a *dog* can be seen, heard, and felt). We hypothesize that a powerful representation is one that models view-invariant factors. Based on this hypothesis, we investigate a contrastive coding scheme, in which a representation is learned that aims to maximize mutual information between different views but is otherwise compact. Our approach scales to any number of views, and is view-agnostic. The resulting learned representations perform above the state of the art for downstream tasks such as object classification, compared to formulations based on predictive learning or single view reconstruction, and improve as more views are added. On the Imagenet linear readoff benchmark, we achieve 68.4% top-1 accuracy.
This interesting paper on an important topic; however, its readability could be dramatically improved, especially for the reader less familiar with the problem. In order to make the paper more accessible, the authors should reorganize the introduction by breaking it down into two parts: 1) a more traditional introduction - one intuitive paragraph about multi-view coding - one intuitive paragraph with an illustrative example on how the proposed approach will help solve a problem; at the same intuitive level, compare-and-contrast it with existing approaches - one intuitive, in-detail paragraph on how the proposed approach works - one paragraph summarizing the main findings/results 2) a second, new section, that will turn the current Figures 1 & 2 into a complete description of an illustrative example (the current, detailed *captions* are a good start, but they should be fleshed out into a full, detailed section of the paper)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Contrastive Multiview Coding
Humans view the world through many sensory channels, e.g., the long-wavelength light channel, viewed by the left eye, or the high-frequency vibrations channel, heard by the right ear. Each view is noisy and incomplete, but important factors, such as physics, geometry, and semantics, tend to be shared between all views (e.g., a *dog* can be seen, heard, and felt). We hypothesize that a powerful representation is one that models view-invariant factors. Based on this hypothesis, we investigate a contrastive coding scheme, in which a representation is learned that aims to maximize mutual information between different views but is otherwise compact. Our approach scales to any number of views, and is view-agnostic. The resulting learned representations perform above the state of the art for downstream tasks such as object classification, compared to formulations based on predictive learning or single view reconstruction, and improve as more views are added. On the Imagenet linear readoff benchmark, we achieve 68.4% top-1 accuracy.
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 interesting paper on an important topic; however, its readability could be dramatically improved, especially for the reader less familiar with the problem. In order to make the paper more accessible, the authors should reorganize the introduction by breaking it down into two parts: 1) a more traditional introduction - one intuitive paragraph about multi-view coding - one intuitive paragraph with an illustrative example on how the proposed approach will help solve a problem; at the same intuitive level, compare-and-contrast it with existing approaches - one intuitive, in-detail paragraph on how the proposed approach works - one paragraph summarizing the main findings/results 2) a second, new section, that will turn the current Figures 1 & 2 into a complete description of an illustrative example (the current, detailed *captions* are a good start, but they should be fleshed out into a full, detailed section of the paper)
assistant
This interesting paper on an important topic; however, its readability could be dramatically improved, especially for the reader less familiar with the problem. In order to make the paper more accessible, the authors should reorganize the introduction by breaking it down into two parts: 1) a more traditional introduction - one intuitive paragraph about multi-view coding - one intuitive paragraph with an illustrative example on how the proposed approach will help solve a problem; at the same intuitive level, compare-and-contrast it with existing approaches - one intuitive, in-detail paragraph on how the proposed approach works - one paragraph summarizing the main findings/results 2) a second, new section, that will turn the current Figures 1 & 2 into a complete description of an illustrative example (the current, detailed *captions* are a good start, but they should be fleshed out into a full, detailed section of the paper)