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 1 . 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 (Yu & Huang, 2019) under different computation constraints, and outperforms the best compound scale model of EfficientNet (Tan & Le, 2019) by 1.5%. The superiority of our framework is also validated on COCO object detection and instance segmentation as well as transfer learning. Deep neural networks have triumphed over various perception tasks such as image classification (He et al., 2016;Huang et al., 2017b;Simonyan & Zisserman, 2014), object detection (Ren et al., 2015;Redmon et al., 2016) and semantic segmentation . However, deep networks usually require large computational resources, making them hard to deploy on mobile devices and embedded systems. This motivates research in reducing the redundancy in deep neural networks or designing light-weight structures. Specifically, MobileNet (Howard et al., 2017) factorizes a standard 3 × 3 convolution into a 3×3 depth-wise convolution and a 1×1 point-wise convolution. ShuffleNet (Zhang et al., 2018a) uses 1 × 1 group convolution to further reduce computations and proposes the shuffle operation to help information flow among different groups. Another kind of approach is to prune redundant connections in the networks. 1 Under review as a conference paper at ICLR 2020 in-network structure for multiple resources.  shares weights among different subnetworks and each sub-network has its own batch normalization layer. US-Net (Yu & Huang, 2019)   proposes to compute batch normalization statistics after training, and introduces two training techniques to train a network that is executable at any network widths. However, these works approach the problem only from network structure perspective, ignoring the importance of network input.
This paper proposes a multi-resolution training scheme for a slimmable network. The proposed method provides a new regime leveraging diverse image resolutions for training sub-networks, which enables efficient network scaling. Throughout the experiments, the authors claim the proposed method shows better performance compared to US-net.
Pros)
(+) The idea of multi-resolution training combining to a slimmable network looks good
(+) Applying a slimmable network's technique to other tasks including detection and segmentation looks good
(+) The combination of multi-resolution and the slimmable network seems to be reasonable.
(+) The paper is well written and looks justified well.
(+) The authors provided extensive experiments.
Cons)
(-) There is no backups why the proposed method could outperform over US-net. 
(-) The proposed method is incremental and improvements are marginal.
(-) Looks like there exists missing in details of the experiments.
(-) The performance report of the compared methods is quite strange.
Comments)
- The proposed method is too straightforward, so the authors should clarify why it works over US-net. Additionally, can the authors provide advantages using a different image-scale need for training a different sub-network? 
- The authors should clarify the training details of US-Net used in this paper. The performance of US-Net in Figure 4 (a) looks the same as the performance of US-Net trained with [0.05, 1]x scaling in the original paper. However, in the original paper, the authors of US-net reported [0.05, 1]x scaling as the worst performance setting in the original paper. Therefore, the authors should compare their method with the best performance setting of US-Net, which is [0.25, 1]x (because the proposed method looks being used [0.25, 1]x training setting, so the comparison should be done in fair).
- The scaling parameters of US-Net used in the experiments should be specified. All the results of US-Net do not contain where they come from (i.e., the training width bound in US-net).
- Can the authors report the results for 0.5-224 and 0.15-224 in Figure.4(a)? Why 0.7-160 and 0.25-160 were picked?
- In Table 1, the performance of EfficientNet is weird. EfficientNet-B2 has 79.8% accuracy with 1.0B FLOPs, but the reported performance in this paper of EfficientNet has 75.6% accuracy with 2.3B FLOPs. Please clarify this.
- How much does KLdiv contribute to the overall performance?
- All the tables are not clearly shown. Please reattach all the tables for better readability.
About rating)
I think the idea looks novel, but the method is quite straightforward, and the paper does not incorporate any analysis as a backup for the proposed method. The initial rating is towards reject, but I would like to see the authors' response and the other reviewers' comments. After that, the final rating might be changed.