A closer look at network resolution for efficient network design
There is growing interest in designing lightweight neural networks for mobile and embedded vision applications. Previous works typically reduce computations from the structure level. For example, group convolution based methods reduce computations by factorizing a vanilla convolution into depth-wise and point-wise convolutions. Pruning based methods prune redundant connections in the network structure. In this paper, we explore the importance of network input for achieving optimal accuracy-efficiency trade-off. Reducing input scale is a simple yet effective way to reduce computational cost. It does not require careful network module design, specific hardware optimization and network retraining after pruning. Moreover, different input scales contain different representations to learn. We propose a framework to mutually learn from different input resolutions and network widths. With the shared knowledge, our framework is able to find better width-resolution balance and capture multi-scale representations. It achieves consistently better ImageNet top-1 accuracy over US-Net under different computation constraints, and outperforms the best compound scale model of EfficientNet by 1.5%. The superiority of our framework is also validated on COCO object detection and instance segmentation as well as transfer learning.
The authors propose a new training regime for multi-resolution slimmable networks. Their approach is based on US-Net training technique but in addition to sampling from different network widths they also sample from different input resolutions and show that using multi-scale inputs improves the top-1 accuracy on ImageNet comparing to US-Net or MobileNet v1/v2 within the same resource constraints. Pros: + The authors correctly identify input resolution as one of the aspects of lightweight network design that is often overlooked + They propose a practically viable training scheme that can be used to train & select networks given resource constraints + The paper is well written and includes many insightful experimental findings Con: The authors specify the mutual learning from width and resolution as their main contribution. They insist that treating input resolution independently from network structure is what distinguishes previous work from the newly suggested technique. But the paper doesn*t include extensive experimental comparisons with the approaches that treat input resolution independently. Thus its claim that joint width/resolution sampling is beneficial comparing to independent approaches is somewhat unfounded. For example, the authors show that MobileNet with 1.0-224 config (no sampling from widths nor from input resolutions during training) is outperformed by their network with 1.0-224 config (which effectively samples only from input resolutions during training). This is not surprising as one can view sampling from input resolutions as an equivalent to data augmentation. The importance of data augmentation is well known, so to prove the proposed mutual learning is beneficial the authors would need to compare against the networks that were trained using this multi-scale data augmentation. Figure 5 has a similar comparison but the only multi-resolution baseline there is US-Net+ which isn*t using multi-resolution images in training. The paper would greatly benefit from adding such comparisons and proving they are not marginal. On rating: I*d summarize the idea of this paper as A) US-Net + B) multi-scale data augmentation + C) selecting the best network based on both input resolution and width to achieve optimal performance within resource constraints. Although C is practical and novel contribution, it is also quite straightforward. I would like to see authors response on how their approach differs from US-Net + multi-scale data augmentation for training and how/why this works better.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
A closer look at network resolution for efficient network design
There is growing interest in designing lightweight neural networks for mobile and embedded vision applications. Previous works typically reduce computations from the structure level. For example, group convolution based methods reduce computations by factorizing a vanilla convolution into depth-wise and point-wise convolutions. Pruning based methods prune redundant connections in the network structure. In this paper, we explore the importance of network input for achieving optimal accuracy-efficiency trade-off. Reducing input scale is a simple yet effective way to reduce computational cost. It does not require careful network module design, specific hardware optimization and network retraining after pruning. Moreover, different input scales contain different representations to learn. We propose a framework to mutually learn from different input resolutions and network widths. With the shared knowledge, our framework is able to find better width-resolution balance and capture multi-scale representations. It achieves consistently better ImageNet top-1 accuracy over US-Net under different computation constraints, and outperforms the best compound scale model of EfficientNet by 1.5%. The superiority of our framework is also validated on COCO object detection and instance segmentation as well as transfer learning.
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 propose a new training regime for multi-resolution slimmable networks. Their approach is based on US-Net training technique but in addition to sampling from different network widths they also sample from different input resolutions and show that using multi-scale inputs improves the top-1 accuracy on ImageNet comparing to US-Net or MobileNet v1/v2 within the same resource constraints. Pros: + The authors correctly identify input resolution as one of the aspects of lightweight network design that is often overlooked + They propose a practically viable training scheme that can be used to train & select networks given resource constraints + The paper is well written and includes many insightful experimental findings Con: The authors specify the mutual learning from width and resolution as their main contribution. They insist that treating input resolution independently from network structure is what distinguishes previous work from the newly suggested technique. But the paper doesn*t include extensive experimental comparisons with the approaches that treat input resolution independently. Thus its claim that joint width/resolution sampling is beneficial comparing to independent approaches is somewhat unfounded. For example, the authors show that MobileNet with 1.0-224 config (no sampling from widths nor from input resolutions during training) is outperformed by their network with 1.0-224 config (which effectively samples only from input resolutions during training). This is not surprising as one can view sampling from input resolutions as an equivalent to data augmentation. The importance of data augmentation is well known, so to prove the proposed mutual learning is beneficial the authors would need to compare against the networks that were trained using this multi-scale data augmentation. Figure 5 has a similar comparison but the only multi-resolution baseline there is US-Net+ which isn*t using multi-resolution images in training. The paper would greatly benefit from adding such comparisons and proving they are not marginal. On rating: I*d summarize the idea of this paper as A) US-Net + B) multi-scale data augmentation + C) selecting the best network based on both input resolution and width to achieve optimal performance within resource constraints. Although C is practical and novel contribution, it is also quite straightforward. I would like to see authors response on how their approach differs from US-Net + multi-scale data augmentation for training and how/why this works better.
assistant
The authors propose a new training regime for multi-resolution slimmable networks. Their approach is based on US-Net training technique but in addition to sampling from different network widths they also sample from different input resolutions and show that using multi-scale inputs improves the top-1 accuracy on ImageNet comparing to US-Net or MobileNet v1/v2 within the same resource constraints. Pros: + The authors correctly identify input resolution as one of the aspects of lightweight network design that is often overlooked + They propose a practically viable training scheme that can be used to train & select networks given resource constraints + The paper is well written and includes many insightful experimental findings Con: The authors specify the mutual learning from width and resolution as their main contribution. They insist that treating input resolution independently from network structure is what distinguishes previous work from the newly suggested technique. But the paper doesn*t include extensive experimental comparisons with the approaches that treat input resolution independently. Thus its claim that joint width/resolution sampling is beneficial comparing to independent approaches is somewhat unfounded. For example, the authors show that MobileNet with 1.0-224 config (no sampling from widths nor from input resolutions during training) is outperformed by their network with 1.0-224 config (which effectively samples only from input resolutions during training). This is not surprising as one can view sampling from input resolutions as an equivalent to data augmentation. The importance of data augmentation is well known, so to prove the proposed mutual learning is beneficial the authors would need to compare against the networks that were trained using this multi-scale data augmentation. Figure 5 has a similar comparison but the only multi-resolution baseline there is US-Net+ which isn*t using multi-resolution images in training. The paper would greatly benefit from adding such comparisons and proving they are not marginal. On rating: I*d summarize the idea of this paper as A) US-Net + B) multi-scale data augmentation + C) selecting the best network based on both input resolution and width to achieve optimal performance within resource constraints. Although C is practical and novel contribution, it is also quite straightforward. I would like to see authors response on how their approach differs from US-Net + multi-scale data augmentation for training and how/why this works better.