An experimental study of layer-level training speed and its impact on generalization
How optimization influences the generalization ability of a DNN is still an active area of research. This work aims to unveil and study a factor of influence: the speed at which each layer trains. In our preliminary work, we develop a visualization technique and an optimization algorithm to monitor and control the layer rotation rate, a tentative measure of layer-level training speed, and show that it has a remarkably consistent and substantial impact on generalization. Our experiments further suggest that weight decay*s and adaptive gradients methods* impact on both generalization performance and speed of convergence are solely due to layer rotation rate changes compared to vanilla SGD, offering a novel interpretation of these widely used techniques, and providing supplementary evidence that layer-level training speed indeed impacts generalization. Besides these fundamental findings, we also expect that on a practical level, the tools we introduce will reduce the meta-parameter tuning required to get the best generalization out of a deep network.
 This paper insists layer-level training speed is crucial for generalization ability. The layer-level training speed is measured by angle between weights at different time stamps in this paper. To control the amount of weight rotation, which means the degree of angle movement, this paper proposes a new algorithm, Layca. This algorithm projects the gradient vector of SGD (or update vector of other variants) onto the space orthogonal to the current weight vector, and adjust the length of the update vector to achieve the desirable angle movement. This paper conducted several experiments to verify the helpfulness of Layca. This paper have an impressive theme, the layer-level training speed is important to have a strong generalization power for CNNs. To verify this hypothesis, this paper proposes a simply SGD-variant to control the amount of weight rotation for showing its impact on generalization. This experimental study shows many insights about how the amount of weight rotation affect the generalization power of CNN family. However, the contribution of this paper is limited. I thought this paper lacks the discussion of how much the layer-level training speed is important. This paper shows the Figure 1 as one clue, but this figure shows the importance of each layer for generalization, not the importance of the layer-level training speed. It is better to show how and how much it is important to consider the layer-level training speed carefully, especially compared with the current state-of-the-art CNN optimization methods or plain SGD (like performance difference). In addition, figures shown in this paper are quite hard to read. Too many figures, too many lines, no legends, and these lines are heavily overlapped. If this paper is accepted and will be published, I strongly recommend authors choose some important figures and lines to make these visible, and move others to supplementary material.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
An experimental study of layer-level training speed and its impact on generalization
How optimization influences the generalization ability of a DNN is still an active area of research. This work aims to unveil and study a factor of influence: the speed at which each layer trains. In our preliminary work, we develop a visualization technique and an optimization algorithm to monitor and control the layer rotation rate, a tentative measure of layer-level training speed, and show that it has a remarkably consistent and substantial impact on generalization. Our experiments further suggest that weight decay*s and adaptive gradients methods* impact on both generalization performance and speed of convergence are solely due to layer rotation rate changes compared to vanilla SGD, offering a novel interpretation of these widely used techniques, and providing supplementary evidence that layer-level training speed indeed impacts generalization. Besides these fundamental findings, we also expect that on a practical level, the tools we introduce will reduce the meta-parameter tuning required to get the best generalization out of a deep network.
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 insists layer-level training speed is crucial for generalization ability. The layer-level training speed is measured by angle between weights at different time stamps in this paper. To control the amount of weight rotation, which means the degree of angle movement, this paper proposes a new algorithm, Layca. This algorithm projects the gradient vector of SGD (or update vector of other variants) onto the space orthogonal to the current weight vector, and adjust the length of the update vector to achieve the desirable angle movement. This paper conducted several experiments to verify the helpfulness of Layca. This paper have an impressive theme, the layer-level training speed is important to have a strong generalization power for CNNs. To verify this hypothesis, this paper proposes a simply SGD-variant to control the amount of weight rotation for showing its impact on generalization. This experimental study shows many insights about how the amount of weight rotation affect the generalization power of CNN family. However, the contribution of this paper is limited. I thought this paper lacks the discussion of how much the layer-level training speed is important. This paper shows the Figure 1 as one clue, but this figure shows the importance of each layer for generalization, not the importance of the layer-level training speed. It is better to show how and how much it is important to consider the layer-level training speed carefully, especially compared with the current state-of-the-art CNN optimization methods or plain SGD (like performance difference). In addition, figures shown in this paper are quite hard to read. Too many figures, too many lines, no legends, and these lines are heavily overlapped. If this paper is accepted and will be published, I strongly recommend authors choose some important figures and lines to make these visible, and move others to supplementary material.
assistant
 This paper insists layer-level training speed is crucial for generalization ability. The layer-level training speed is measured by angle between weights at different time stamps in this paper. To control the amount of weight rotation, which means the degree of angle movement, this paper proposes a new algorithm, Layca. This algorithm projects the gradient vector of SGD (or update vector of other variants) onto the space orthogonal to the current weight vector, and adjust the length of the update vector to achieve the desirable angle movement. This paper conducted several experiments to verify the helpfulness of Layca. This paper have an impressive theme, the layer-level training speed is important to have a strong generalization power for CNNs. To verify this hypothesis, this paper proposes a simply SGD-variant to control the amount of weight rotation for showing its impact on generalization. This experimental study shows many insights about how the amount of weight rotation affect the generalization power of CNN family. However, the contribution of this paper is limited. I thought this paper lacks the discussion of how much the layer-level training speed is important. This paper shows the Figure 1 as one clue, but this figure shows the importance of each layer for generalization, not the importance of the layer-level training speed. It is better to show how and how much it is important to consider the layer-level training speed carefully, especially compared with the current state-of-the-art CNN optimization methods or plain SGD (like performance difference). In addition, figures shown in this paper are quite hard to read. Too many figures, too many lines, no legends, and these lines are heavily overlapped. If this paper is accepted and will be published, I strongly recommend authors choose some important figures and lines to make these visible, and move others to supplementary material.