Using Word Embeddings to Explore the Learned Representations of Convolutional Neural Networks
As deep neural net architectures minimize loss, they build up information in a hierarchy of learned representations that ultimately serve their final goal. Different architectures tackle this problem in slightly different ways, but all models aim to create representational spaces that accumulate information through the depth of the network. Here we build on previous work that indicated that two very different model classes trained on two very different tasks actually build knowledge representations that have similar underlying representations. Namely, we compare word embeddings from SkipGram (trained to predict co-occurring words) to several CNN architectures (trained for image classification) in order to understand how this accumulation of knowledge behaves in CNNs. We improve upon previous work by including 5 times more ImageNet classes in our experiments, and further expand the scope of the analyses to include a network trained on CIFAR-100. We characterize network behavior in pretrained models, and also during training, misclassification, and adversarial attack. Our work illustrates the power of using one model to explore another, gives new insights for CNN models, and provides a framework for others to perform similar analyses when developing new architectures.
 This paper extends the previous work (Dharmaretnam & Fyshe, 2018), which provided a analytic tool for understanding CNNs through word embeddings of class labels. By analyzing correlations between each CNN layers and class labels, enables to investigates how each layer of CNNs work, how much it performed well, or how to improve the performance. I felt it is little hard to read this paper. Although the short summary of contributions of this paper in the Introduction, I could not easily distinguish contributions of this paper from the ones of the previous work. It*s better to explicitly explain which part is the contributions of this paper in detail. For example, "additional explorations of the behavior of the hidden layers during training" is not clear to me because this expression only explain what this paper do briefly, not what this paper is actually different from the previous work, and how this difference is important and crucial. Similarly, I could not understand why adding concepts, architectures (FractalNet), datasets (CIFAR-100) is so important. Although this paper states these changes are one of the contributions, it is unclear whether these changes lead to significant insights and findings which the previous work could not find, and whether these findings are so important as contributions of this paper. Again, I think it is better to describe what main contributions of this paper are in more detail.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Using Word Embeddings to Explore the Learned Representations of Convolutional Neural Networks
As deep neural net architectures minimize loss, they build up information in a hierarchy of learned representations that ultimately serve their final goal. Different architectures tackle this problem in slightly different ways, but all models aim to create representational spaces that accumulate information through the depth of the network. Here we build on previous work that indicated that two very different model classes trained on two very different tasks actually build knowledge representations that have similar underlying representations. Namely, we compare word embeddings from SkipGram (trained to predict co-occurring words) to several CNN architectures (trained for image classification) in order to understand how this accumulation of knowledge behaves in CNNs. We improve upon previous work by including 5 times more ImageNet classes in our experiments, and further expand the scope of the analyses to include a network trained on CIFAR-100. We characterize network behavior in pretrained models, and also during training, misclassification, and adversarial attack. Our work illustrates the power of using one model to explore another, gives new insights for CNN models, and provides a framework for others to perform similar analyses when developing new architectures.
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 extends the previous work (Dharmaretnam & Fyshe, 2018), which provided a analytic tool for understanding CNNs through word embeddings of class labels. By analyzing correlations between each CNN layers and class labels, enables to investigates how each layer of CNNs work, how much it performed well, or how to improve the performance. I felt it is little hard to read this paper. Although the short summary of contributions of this paper in the Introduction, I could not easily distinguish contributions of this paper from the ones of the previous work. It*s better to explicitly explain which part is the contributions of this paper in detail. For example, "additional explorations of the behavior of the hidden layers during training" is not clear to me because this expression only explain what this paper do briefly, not what this paper is actually different from the previous work, and how this difference is important and crucial. Similarly, I could not understand why adding concepts, architectures (FractalNet), datasets (CIFAR-100) is so important. Although this paper states these changes are one of the contributions, it is unclear whether these changes lead to significant insights and findings which the previous work could not find, and whether these findings are so important as contributions of this paper. Again, I think it is better to describe what main contributions of this paper are in more detail.
assistant
 This paper extends the previous work (Dharmaretnam & Fyshe, 2018), which provided a analytic tool for understanding CNNs through word embeddings of class labels. By analyzing correlations between each CNN layers and class labels, enables to investigates how each layer of CNNs work, how much it performed well, or how to improve the performance. I felt it is little hard to read this paper. Although the short summary of contributions of this paper in the Introduction, I could not easily distinguish contributions of this paper from the ones of the previous work. It*s better to explicitly explain which part is the contributions of this paper in detail. For example, "additional explorations of the behavior of the hidden layers during training" is not clear to me because this expression only explain what this paper do briefly, not what this paper is actually different from the previous work, and how this difference is important and crucial. Similarly, I could not understand why adding concepts, architectures (FractalNet), datasets (CIFAR-100) is so important. Although this paper states these changes are one of the contributions, it is unclear whether these changes lead to significant insights and findings which the previous work could not find, and whether these findings are so important as contributions of this paper. Again, I think it is better to describe what main contributions of this paper are in more detail.