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 paper under consideration proposes a set of procedures for incrementally expanding a residual network by adding layers via a boosting criterion. The main barrier to publication is the weak empirical validation. The tasks considered are quite small scale in 2016 (and MNIST with a convolutional net is basically an uninteresting test by this point). The paper doesn*t compare to the literature, and CIFAR-10 results fail to improve upon rather simple, single-network published baselines (Springenberg et al, 2015 for example, obtains 92% without data augmentation) and I*m pretty sure there*s a simple ResNet result somewhere that outshines these too. The CIFAR100 results are a little bit interesting as they are better than I*m used to seeing (I haven*t done a recent literature crawl), and this is unsurprising -- you*d expect ensembles to do well when there*s a dearth of labeled training data, and here there are only a few hundred per label. But then it*s typical on both CIFAR10 and CIFAR100 to use simple data augmentation schemes which aren*t employed here, and these and other forms of regularization are a simpler alternative to a complicated iterative augmentation scheme like this. It*d be easier to sell this method either as an option for scarce labeled datasets where data augmentation is non-trivial (but then for most image-related applications, random crops and reflections are easy and valid), but that would necessitate different benchmarks, and comparison against simpler methods like said data augmentation, dropout (especially, due to the ensemble interpretation), and so on.
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 paper under consideration proposes a set of procedures for incrementally expanding a residual network by adding layers via a boosting criterion. The main barrier to publication is the weak empirical validation. The tasks considered are quite small scale in 2016 (and MNIST with a convolutional net is basically an uninteresting test by this point). The paper doesn*t compare to the literature, and CIFAR-10 results fail to improve upon rather simple, single-network published baselines (Springenberg et al, 2015 for example, obtains 92% without data augmentation) and I*m pretty sure there*s a simple ResNet result somewhere that outshines these too. The CIFAR100 results are a little bit interesting as they are better than I*m used to seeing (I haven*t done a recent literature crawl), and this is unsurprising -- you*d expect ensembles to do well when there*s a dearth of labeled training data, and here there are only a few hundred per label. But then it*s typical on both CIFAR10 and CIFAR100 to use simple data augmentation schemes which aren*t employed here, and these and other forms of regularization are a simpler alternative to a complicated iterative augmentation scheme like this. It*d be easier to sell this method either as an option for scarce labeled datasets where data augmentation is non-trivial (but then for most image-related applications, random crops and reflections are easy and valid), but that would necessitate different benchmarks, and comparison against simpler methods like said data augmentation, dropout (especially, due to the ensemble interpretation), and so on.
assistant
The paper under consideration proposes a set of procedures for incrementally expanding a residual network by adding layers via a boosting criterion. The main barrier to publication is the weak empirical validation. The tasks considered are quite small scale in 2016 (and MNIST with a convolutional net is basically an uninteresting test by this point). The paper doesn*t compare to the literature, and CIFAR-10 results fail to improve upon rather simple, single-network published baselines (Springenberg et al, 2015 for example, obtains 92% without data augmentation) and I*m pretty sure there*s a simple ResNet result somewhere that outshines these too. The CIFAR100 results are a little bit interesting as they are better than I*m used to seeing (I haven*t done a recent literature crawl), and this is unsurprising -- you*d expect ensembles to do well when there*s a dearth of labeled training data, and here there are only a few hundred per label. But then it*s typical on both CIFAR10 and CIFAR100 to use simple data augmentation schemes which aren*t employed here, and these and other forms of regularization are a simpler alternative to a complicated iterative augmentation scheme like this. It*d be easier to sell this method either as an option for scarce labeled datasets where data augmentation is non-trivial (but then for most image-related applications, random crops and reflections are easy and valid), but that would necessitate different benchmarks, and comparison against simpler methods like said data augmentation, dropout (especially, due to the ensemble interpretation), and so on.