Boosted Residual Networks
In this paper we present a new ensemble method, called Boosted Residual Networks, which builds an ensemble of Residual Networks by growing the member network at each round of boosting. The proposed approach combines recent developements in Residual Networks - a method for creating very deep networks by including a shortcut layer between different groups of layers - with the Deep Incremental Boosting, which has been proposed as a methodology to train fast ensembles of networks of increasing depth through the use of boosting. We demonstrate that the synergy of Residual Networks and Deep Incremental Boosting has better potential than simply boosting a Residual Network of fixed structure or using the equivalent Deep Incremental Boosting without the shortcut layers.
The authors mention that they are not aiming to have SOTA results. However, that an ensemble of resnets has lower performance than some of single network results, indicates that further experimentation preferably on larger datasets is necessary. The literature review could at least mention some existing works such as wide resnets https://arxiv.org/abs/1605.07146 or the ones that use knowledge distillation for ensemble of networks for comparison on cifar. While the manuscript is well-written and the idea is novel, it needs to be extended with experiments.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Boosted Residual Networks
In this paper we present a new ensemble method, called Boosted Residual Networks, which builds an ensemble of Residual Networks by growing the member network at each round of boosting. The proposed approach combines recent developements in Residual Networks - a method for creating very deep networks by including a shortcut layer between different groups of layers - with the Deep Incremental Boosting, which has been proposed as a methodology to train fast ensembles of networks of increasing depth through the use of boosting. We demonstrate that the synergy of Residual Networks and Deep Incremental Boosting has better potential than simply boosting a Residual Network of fixed structure or using the equivalent Deep Incremental Boosting without the shortcut layers.
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 mention that they are not aiming to have SOTA results. However, that an ensemble of resnets has lower performance than some of single network results, indicates that further experimentation preferably on larger datasets is necessary. The literature review could at least mention some existing works such as wide resnets https://arxiv.org/abs/1605.07146 or the ones that use knowledge distillation for ensemble of networks for comparison on cifar. While the manuscript is well-written and the idea is novel, it needs to be extended with experiments.
assistant
The authors mention that they are not aiming to have SOTA results. However, that an ensemble of resnets has lower performance than some of single network results, indicates that further experimentation preferably on larger datasets is necessary. The literature review could at least mention some existing works such as wide resnets https://arxiv.org/abs/1605.07146 or the ones that use knowledge distillation for ensemble of networks for comparison on cifar. While the manuscript is well-written and the idea is novel, it needs to be extended with experiments.