Towards Interpreting Deep Neural Networks via Understanding Layer Behaviors
Deep neural networks (DNNs) have achieved unprecedented practical success in many applications. However, how to interpret DNNs is still an open problem. In particular, what do hidden layers behave is not clearly understood. In this paper, relying on a teacher-student paradigm, we seek to understand the layer behaviors of DNNs by ``monitoring* both across-layer and single-layer distribution evolution to some target distribution in the training. Here, the ``across-layer* and ``single-layer* considers the layer behavior emph{along the depth} and a specific layer emph{along training epochs}, respectively. Relying on optimal transport theory, we employ the Wasserstein distance ( -distance) to measure the divergence between the layer distribution and the target distribution. Theoretically, we prove that i) the -distance of across layers to the target distribution tends to decrease along the depth. ii) the -distance of a specific layer to the target distribution tends to decrease along training iterations. iii) However, a deep layer is not always better than a shallow layer for some samples. Moreover, our results helps to analyze the stability of layer distributions and explains why auxiliary losses helps the training of DNNs. Extensive experiments on real-world datasets justify our theoretical findings.
The authors intuitively, and then analytically, explain the behavior in the hidden layers of deep convolutional networks and show how the behavior can be used to improve performance by *early exiting.* I give this paper a weak reject. I believe this paper does well by connecting the intuitive explanation with the proofs, and then by confirming their results through experimentation. I also applaud the authors for their rigorous explanation of the hyper-parameters and experimentation methods. However, from what I can tell, there was no cross-fold validation or even repeat trials with different partitioning to see whether the differences in performance were just random perturbations or a consistent effect. The increase in accuracy isn*t large enough across experiments to allay my concerns. I think the authors have some very compelling work here, but the lack of a large difference in accuracy combined with insufficient testing methodology causes me to reject this paper... but only barely. I can be convinced otherwise with a compelling set of arguments.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Towards Interpreting Deep Neural Networks via Understanding Layer Behaviors
Deep neural networks (DNNs) have achieved unprecedented practical success in many applications. However, how to interpret DNNs is still an open problem. In particular, what do hidden layers behave is not clearly understood. In this paper, relying on a teacher-student paradigm, we seek to understand the layer behaviors of DNNs by ``monitoring* both across-layer and single-layer distribution evolution to some target distribution in the training. Here, the ``across-layer* and ``single-layer* considers the layer behavior emph{along the depth} and a specific layer emph{along training epochs}, respectively. Relying on optimal transport theory, we employ the Wasserstein distance ( -distance) to measure the divergence between the layer distribution and the target distribution. Theoretically, we prove that i) the -distance of across layers to the target distribution tends to decrease along the depth. ii) the -distance of a specific layer to the target distribution tends to decrease along training iterations. iii) However, a deep layer is not always better than a shallow layer for some samples. Moreover, our results helps to analyze the stability of layer distributions and explains why auxiliary losses helps the training of DNNs. Extensive experiments on real-world datasets justify our theoretical findings.
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 authors intuitively, and then analytically, explain the behavior in the hidden layers of deep convolutional networks and show how the behavior can be used to improve performance by *early exiting.* I give this paper a weak reject. I believe this paper does well by connecting the intuitive explanation with the proofs, and then by confirming their results through experimentation. I also applaud the authors for their rigorous explanation of the hyper-parameters and experimentation methods. However, from what I can tell, there was no cross-fold validation or even repeat trials with different partitioning to see whether the differences in performance were just random perturbations or a consistent effect. The increase in accuracy isn*t large enough across experiments to allay my concerns. I think the authors have some very compelling work here, but the lack of a large difference in accuracy combined with insufficient testing methodology causes me to reject this paper... but only barely. I can be convinced otherwise with a compelling set of arguments.
assistant
The authors intuitively, and then analytically, explain the behavior in the hidden layers of deep convolutional networks and show how the behavior can be used to improve performance by *early exiting. * I give this paper a weak reject. I believe this paper does well by connecting the intuitive explanation with the proofs, and then by confirming their results through experimentation. I also applaud the authors for their rigorous explanation of the hyper-parameters and experimentation methods. However, from what I can tell, there was no cross-fold validation or even repeat trials with different partitioning to see whether the differences in performance were just random perturbations or a consistent effect. The increase in accuracy isn*t large enough across experiments to allay my concerns. I think the authors have some very compelling work here, but the lack of a large difference in accuracy combined with insufficient testing methodology causes me to reject this paper... but only barely. I can be convinced otherwise with a compelling set of arguments.