Modularized Morphing of Neural Networks
In this work we study the problem of network morphism, an effective learning scheme to morph a well-trained neural network to a new one with the network function completely preserved. Different from existing work where basic morphing types on the layer level were addressed, we target at the central problem of network morphism at a higher level, i.e., how a convolutional layer can be morphed into an arbitrary module of a neural network. To simplify the representation of a network, we abstract a module as a graph with blobs as vertices and convolutional layers as edges, based on which the morphing process is able to be formulated as a graph transformation problem. Two atomic morphing operations are introduced to compose the graphs, based on which modules are classified into two families, i.e., simple morphable modules and complex modules. We present practical morphing solutions for both of these two families, and prove that any reasonable module can be morphed from a single convolutional layer. Extensive experiments have been conducted based on the state-of-the-art ResNet on benchmark datasets, and the effectiveness of the proposed solution has been verified.
The paper proposes a methodology for morphing a trained network to different architecture without having to retrain from scratch. The manuscript reads well and the description is easy to follow. However, the results are not very convincing as the selected baselines are considerably far from the state of the art. The paper should include comparisons with state of the art, for example wide residual networks. Tables should also report number of parameters for each architecture, this would help fair comparison.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Modularized Morphing of Neural Networks
In this work we study the problem of network morphism, an effective learning scheme to morph a well-trained neural network to a new one with the network function completely preserved. Different from existing work where basic morphing types on the layer level were addressed, we target at the central problem of network morphism at a higher level, i.e., how a convolutional layer can be morphed into an arbitrary module of a neural network. To simplify the representation of a network, we abstract a module as a graph with blobs as vertices and convolutional layers as edges, based on which the morphing process is able to be formulated as a graph transformation problem. Two atomic morphing operations are introduced to compose the graphs, based on which modules are classified into two families, i.e., simple morphable modules and complex modules. We present practical morphing solutions for both of these two families, and prove that any reasonable module can be morphed from a single convolutional layer. Extensive experiments have been conducted based on the state-of-the-art ResNet on benchmark datasets, and the effectiveness of the proposed solution has been verified.
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 paper proposes a methodology for morphing a trained network to different architecture without having to retrain from scratch. The manuscript reads well and the description is easy to follow. However, the results are not very convincing as the selected baselines are considerably far from the state of the art. The paper should include comparisons with state of the art, for example wide residual networks. Tables should also report number of parameters for each architecture, this would help fair comparison.
assistant
The paper proposes a methodology for morphing a trained network to different architecture without having to retrain from scratch. The manuscript reads well and the description is easy to follow. However, the results are not very convincing as the selected baselines are considerably far from the state of the art. The paper should include comparisons with state of the art, for example wide residual networks. Tables should also report number of parameters for each architecture, this would help fair comparison.