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.
The authors propose a new method of measuring a knowledge within the learned CNN: the representations of CNN layers and word2vec embeddings and compared, and the similarity between them are calculate. The authors claim that the similarity score increases with learning time, and the higher layers of CNN have more similarity to word2vec embeddings than the lower layers..
CNN and word2vec use different datasets. CNN uses the vision pixels and word2vec uses the words in the sentences. A certain amount of representation patterns can be expected to be shared, but surely the extent is limited (correlation 0.9 in Fig. 1). Because of this limitation, the proposed similarity measure must not be claimed as the measure of knowledge accumulation in CNN. 
In addition, the authors have to be precise in defining the measure and provide the information captured by the measure. In the literature, I can see “something” is shared by the two algorithms but do not know what is this “something.” The authors claim that “semantics” are shared, but replacing “semantics” to “something” does not make any difference in this manuscript. Further investigations and confirmations are needed to report which information is actually similar to each other.
Minor: the 1 vs. 2 accuracy measure is not defined.
In summary, the proposed measure may capture some information but the explanation about this information is unclear. The information seems to be a rough similar pattern of concept representations. Further rigorous investigation of the proposed measure is necessary to confirm which information is captured. The current version is not sufficient for acceptance.