On the Generalization Effects of DenseNet Model Structures
Modern neural network architectures take advantage of increasingly deeper layers, and various advances in their structure to achieve better performance. While traditional explicit regularization techniques like dropout, weight decay, and data augmentation are still being used in these new models, little about the regularization and generalization effects of these new structures have been studied. Besides being deeper than their predecessors, could newer architectures like ResNet and DenseNet also benefit from their structures* implicit regularization properties? In this work, we investigate the skip connection*s effect on network*s generalization features. Through experiments, we show that certain neural network architectures contribute to their generalization abilities. Specifically, we study the effect that low-level features have on generalization performance when they are introduced to deeper layers in DenseNet, ResNet as well as networks with *skip connections*. We show that these low-level representations do help with generalization in multiple settings when both the quality and quantity of training data is decreased.
 The ms analyses a number of simulations how skip connections effect the generalization of different network architectures. The experiments are somewhat interesting but they appear rather preliminary. To indeed show the claims made, error bars in the graphs would be necessary as well will more careful and more generic analysis. In addition clear hypotheses should be stated. The fact that some behaviour is seen in MNIST or CIFAR in the simulations does not permit conclusion for other data sets. Typically extensive teacher student simulations are required to validly make points. Also formally the paper is not in good shape.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
On the Generalization Effects of DenseNet Model Structures
Modern neural network architectures take advantage of increasingly deeper layers, and various advances in their structure to achieve better performance. While traditional explicit regularization techniques like dropout, weight decay, and data augmentation are still being used in these new models, little about the regularization and generalization effects of these new structures have been studied. Besides being deeper than their predecessors, could newer architectures like ResNet and DenseNet also benefit from their structures* implicit regularization properties? In this work, we investigate the skip connection*s effect on network*s generalization features. Through experiments, we show that certain neural network architectures contribute to their generalization abilities. Specifically, we study the effect that low-level features have on generalization performance when they are introduced to deeper layers in DenseNet, ResNet as well as networks with *skip connections*. We show that these low-level representations do help with generalization in multiple settings when both the quality and quantity of training data is decreased.
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 ms analyses a number of simulations how skip connections effect the generalization of different network architectures. The experiments are somewhat interesting but they appear rather preliminary. To indeed show the claims made, error bars in the graphs would be necessary as well will more careful and more generic analysis. In addition clear hypotheses should be stated. The fact that some behaviour is seen in MNIST or CIFAR in the simulations does not permit conclusion for other data sets. Typically extensive teacher student simulations are required to validly make points. Also formally the paper is not in good shape.
assistant
 The ms analyses a number of simulations how skip connections effect the generalization of different network architectures. The experiments are somewhat interesting but they appear rather preliminary. To indeed show the claims made, error bars in the graphs would be necessary as well will more careful and more generic analysis. In addition clear hypotheses should be stated. The fact that some behaviour is seen in MNIST or CIFAR in the simulations does not permit conclusion for other data sets. Typically extensive teacher student simulations are required to validly make points. Also formally the paper is not in good shape.