Pseudosaccades: A simple ensemble scheme for improving classification performance of deep nets
We describe a simple ensemble approach that, unlike conventional ensembles, uses multiple random data sketches (‘pseudosaccades’) rather than multiple classifiers to improve classification performance. Using this simple, but novel, approach we obtain statistically significant improvements in classification performance on AlexNet, GoogLeNet, ResNet-50 and ResNet-152 baselines on Imagenet data – e.g. of the order of 0.3% to 0.6% in Top-1 accuracy and similar improvements in Top-k accuracy – essentially nearly for free.
 Pros: -- Superior empirical results are the key highlights of this paper. -- The experiments are well designed and benchmarked against the state-of-the-art models. Cons: -- One typically uses affine transformations of the training images to improve the performance of the CNN. From that perspective, the paper does not offer any new insight. I am not entirely convinced that this is a novel enough contribution to be accepted in ICLR. -- The "ensemble of ensembles" approach described in Section 3.5 is not clear. -- Overall, the paper does not have much novelty, but the results are quite promising.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Pseudosaccades: A simple ensemble scheme for improving classification performance of deep nets
We describe a simple ensemble approach that, unlike conventional ensembles, uses multiple random data sketches (‘pseudosaccades’) rather than multiple classifiers to improve classification performance. Using this simple, but novel, approach we obtain statistically significant improvements in classification performance on AlexNet, GoogLeNet, ResNet-50 and ResNet-152 baselines on Imagenet data – e.g. of the order of 0.3% to 0.6% in Top-1 accuracy and similar improvements in Top-k accuracy – essentially nearly for free.
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>



 Pros: -- Superior empirical results are the key highlights of this paper. -- The experiments are well designed and benchmarked against the state-of-the-art models. Cons: -- One typically uses affine transformations of the training images to improve the performance of the CNN. From that perspective, the paper does not offer any new insight. I am not entirely convinced that this is a novel enough contribution to be accepted in ICLR. -- The "ensemble of ensembles" approach described in Section 3.5 is not clear. -- Overall, the paper does not have much novelty, but the results are quite promising.
assistant
 Pros: -- Superior empirical results are the key highlights of this paper. -- The experiments are well designed and benchmarked against the state-of-the-art models. Cons: -- One typically uses affine transformations of the training images to improve the performance of the CNN. From that perspective, the paper does not offer any new insight. I am not entirely convinced that this is a novel enough contribution to be accepted in ICLR. -- The "ensemble of ensembles" approach described in Section 3.5 is not clear. -- Overall, the paper does not have much novelty, but the results are quite promising.