ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms (e.g. 10 4 GPU hours) makes it difficult to directly search the architectures on large-scale tasks (e.g. ImageNet). Differentiable NAS can reduce the cost of GPU hours via a continuous representation of network architecture but suffers from the high GPU memory consumption issue (grow linearly w.r.t. candidate set size). As a result, they need to utilize proxy tasks, such as training on a smaller dataset, or learning with only a few blocks, or training just for a few epochs. These architectures optimized on proxy tasks are not guaranteed to be optimal on the target task. In this paper, we present ProxylessNAS that can directly learn the architectures for large-scale target tasks and target hardware platforms. We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candidate set. Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of directness and specialization. On CIFAR-10, our model achieves 2.08% test error with only 5.7M parameters, better than the previous state-of-the-art architecture AmoebaNet-B, while using 6× fewer parameters. On ImageNet, our model achieves 3.1% better top-1 accuracy than MobileNetV2, while being 1.2× faster with measured GPU latency. We also apply ProxylessNAS to specialize neural architectures for hardware with direct hardware metrics (e.g. latency) and provide insights for efficient CNN architecture design.
 It seems the authors propose an efficient method to search platform-aware network architecture aiming at high recognition accuracy and low latency. Their results on CIFAR-10 and ImageNet are surprisingly good. But it is still hard to believe that the author can achieve 2.08% error rate with only 5.7M parameter on CIFAR10 and 74.5% top-1 accuracy on ImageNet with less GPU hours/memories than prior arts. Given my concerns above, the author must release their code and detail pipelines since NAS papers are difficult to be reproduced. There is a small typo in reference part: Jing-Dong Dong*s work should be DPP-Net instead of PPP-Net (https://eccv2018.org/openaccess/content_ECCV_2018/papers/Jin-Dong_Dong_DPP-Net_Device-aware_Progressive_ECCV_2018_paper.pdf) and I think this paper "Neural Architecture Optimization" shoud be cited.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms (e.g. 10 4 GPU hours) makes it difficult to directly search the architectures on large-scale tasks (e.g. ImageNet). Differentiable NAS can reduce the cost of GPU hours via a continuous representation of network architecture but suffers from the high GPU memory consumption issue (grow linearly w.r.t. candidate set size). As a result, they need to utilize proxy tasks, such as training on a smaller dataset, or learning with only a few blocks, or training just for a few epochs. These architectures optimized on proxy tasks are not guaranteed to be optimal on the target task. In this paper, we present ProxylessNAS that can directly learn the architectures for large-scale target tasks and target hardware platforms. We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candidate set. Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of directness and specialization. On CIFAR-10, our model achieves 2.08% test error with only 5.7M parameters, better than the previous state-of-the-art architecture AmoebaNet-B, while using 6× fewer parameters. On ImageNet, our model achieves 3.1% better top-1 accuracy than MobileNetV2, while being 1.2× faster with measured GPU latency. We also apply ProxylessNAS to specialize neural architectures for hardware with direct hardware metrics (e.g. latency) and provide insights for efficient CNN architecture design.
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>



 It seems the authors propose an efficient method to search platform-aware network architecture aiming at high recognition accuracy and low latency. Their results on CIFAR-10 and ImageNet are surprisingly good. But it is still hard to believe that the author can achieve 2.08% error rate with only 5.7M parameter on CIFAR10 and 74.5% top-1 accuracy on ImageNet with less GPU hours/memories than prior arts. Given my concerns above, the author must release their code and detail pipelines since NAS papers are difficult to be reproduced. There is a small typo in reference part: Jing-Dong Dong*s work should be DPP-Net instead of PPP-Net (https://eccv2018.org/openaccess/content_ECCV_2018/papers/Jin-Dong_Dong_DPP-Net_Device-aware_Progressive_ECCV_2018_paper.pdf) and I think this paper "Neural Architecture Optimization" shoud be cited.
assistant
 It seems the authors propose an efficient method to search platform-aware network architecture aiming at high recognition accuracy and low latency. Their results on CIFAR-10 and ImageNet are surprisingly good. But it is still hard to believe that the author can achieve 2.08% error rate with only 5.7M parameter on CIFAR10 and 74.5% top-1 accuracy on ImageNet with less GPU hours/memories than prior arts. Given my concerns above, the author must release their code and detail pipelines since NAS papers are difficult to be reproduced. There is a small typo in reference part: Jing-Dong Dong*s work should be DPP-Net instead of PPP-Net (https://eccv2018.org/openaccess/content_ECCV_2018/papers/Jin-Dong_Dong_DPP-Net_Device-aware_Progressive_ECCV_2018_paper.pdf) and I think this paper "Neural Architecture Optimization" shoud be cited.