Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
Deep Neural Networks (DNNs) excel on many complex perceptual tasks but it has proven notoriously difficult to understand how they reach their decisions. We here introduce a high-performance DNN architecture on ImageNet whose decisions are considerably easier to explain. Our model, a simple variant of the ResNet-50 architecture called BagNet, classifies an image based on the occurrences of small local image features without taking into account their spatial ordering. This strategy is closely related to the bag-of-feature (BoF) models popular before the onset of deep learning and reaches a surprisingly high accuracy on ImageNet (87.6% top-5 for 32 x 32 px features and Alexnet performance for 16 x16 px features). The constraint on local features makes it straight-forward to analyse how exactly each part of the image influences the classification. Furthermore, the BagNets behave similar to state-of-the art deep neural networks such as VGG-16, ResNet-152 or DenseNet-169 in terms of feature sensitivity, error distribution and interactions between image parts. This suggests that the improvements of DNNs over previous bag-of-feature classifiers in the last few years is mostly achieved by better fine-tuning rather than by qualitatively different decision strategies.
 This paper suggests a novel and compact neural network architecture which uses the information within bag-of-words features. The proposed algorithm only uses the patch information independently and performs majority voting using independently classified patches. The proposed method provides the state-of-the-art prediction accuracy unexpectedly, and several additional experiments show the state-of-the-art neural networks mainly learn without association between information in different patches. The proposed algorithm is simple and does not provide completely new idea, but this paper has a clear contribution connecting the previous main idea of feature extraction, bag-of-words, and the prevailing blackbox algorithm, CNN. The results in the paper are worth to be shared in the community and need further investigated. The presented experiments look fair and reasonable to show the importance of the independent patch information (without association between them), and the presented experimental results show some state-of-the-art methods also perform with independent patch information. Comparison with attention models is necessary to compare the important patches obtained from conventional networks.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
Deep Neural Networks (DNNs) excel on many complex perceptual tasks but it has proven notoriously difficult to understand how they reach their decisions. We here introduce a high-performance DNN architecture on ImageNet whose decisions are considerably easier to explain. Our model, a simple variant of the ResNet-50 architecture called BagNet, classifies an image based on the occurrences of small local image features without taking into account their spatial ordering. This strategy is closely related to the bag-of-feature (BoF) models popular before the onset of deep learning and reaches a surprisingly high accuracy on ImageNet (87.6% top-5 for 32 x 32 px features and Alexnet performance for 16 x16 px features). The constraint on local features makes it straight-forward to analyse how exactly each part of the image influences the classification. Furthermore, the BagNets behave similar to state-of-the art deep neural networks such as VGG-16, ResNet-152 or DenseNet-169 in terms of feature sensitivity, error distribution and interactions between image parts. This suggests that the improvements of DNNs over previous bag-of-feature classifiers in the last few years is mostly achieved by better fine-tuning rather than by qualitatively different decision strategies.
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 paper suggests a novel and compact neural network architecture which uses the information within bag-of-words features. The proposed algorithm only uses the patch information independently and performs majority voting using independently classified patches. The proposed method provides the state-of-the-art prediction accuracy unexpectedly, and several additional experiments show the state-of-the-art neural networks mainly learn without association between information in different patches. The proposed algorithm is simple and does not provide completely new idea, but this paper has a clear contribution connecting the previous main idea of feature extraction, bag-of-words, and the prevailing blackbox algorithm, CNN. The results in the paper are worth to be shared in the community and need further investigated. The presented experiments look fair and reasonable to show the importance of the independent patch information (without association between them), and the presented experimental results show some state-of-the-art methods also perform with independent patch information. Comparison with attention models is necessary to compare the important patches obtained from conventional networks.
assistant
 This paper suggests a novel and compact neural network architecture which uses the information within bag-of-words features. The proposed algorithm only uses the patch information independently and performs majority voting using independently classified patches. The proposed method provides the state-of-the-art prediction accuracy unexpectedly, and several additional experiments show the state-of-the-art neural networks mainly learn without association between information in different patches. The proposed algorithm is simple and does not provide completely new idea, but this paper has a clear contribution connecting the previous main idea of feature extraction, bag-of-words, and the prevailing blackbox algorithm, CNN. The results in the paper are worth to be shared in the community and need further investigated. The presented experiments look fair and reasonable to show the importance of the independent patch information (without association between them), and the presented experimental results show some state-of-the-art methods also perform with independent patch information. Comparison with attention models is necessary to compare the important patches obtained from conventional networks.