NAS evaluation is frustratingly hard
Neural Architecture Search (NAS) is an exciting new field which promises to be as much as a game-changer as Convolutional Neural Networks were in 2012. Despite many great works leading to substantial improvements on a variety of tasks, comparison between different methods is still very much an open issue. While most algorithms are tested on the same datasets, there is no shared experimental protocol followed by all. As such, and due to the under-use of ablation studies, there is a lack of clarity regarding why certain methods are more effective than others. Our first contribution is a benchmark of 8 NAS methods on 5 datasets. To overcome the hurdle of comparing methods with different search spaces, we propose using a method’s relative improvement over the randomly sampled average architecture, which effectively removes advantages arising from expertly engineered search spaces or training protocols. Surprisingly, we find that many NAS techniques struggle to significantly beat the average architecture baseline. We perform further experiments with the commonly used DARTS search space in order to understand the contribution of each component in the NAS pipeline. These experiments highlight that: (i) the use of tricks in the evaluation protocol has a predominant impact on the reported performance of architectures; (ii) the cell-based search space has a very narrow accuracy range, such that the seed has a considerable impact on architecture rankings; (iii) the hand-designed macrostructure (cells) is more important than the searched micro-structure (operations); and (iv) the depth-gap is a real phenomenon, evidenced by the change in rankings between 8 and 20 cell architectures. To conclude, we suggest best practices, that we hope will prove useful for the community and help mitigate current NAS pitfalls, e.g. difficulties in reproducibility and comparison of search methods. We provide the code used for our experiments at link-to-come.
In this submission, the authors conduct a series of experiments on five image classification datasets to compare several existing NAS methods. Based on these experimental results, they point out: 1) how a network is trained (i.e., training protocols/tricks such as DropPath, Cutout) plays an important role for the final accuracy; 2) within the search space, the existing NAS methods perform close to or slightly better than a random sampling baseline; 3) hyperparameters of NAS methods also have significant effect on the performance. With these interesting findings, I suggest rejecting this submission. The reasons are as follows: 1) For the first finding of training protocol, several existing papers and books already discussed it, such as Li & Talwalkar (2019) and the book chapter {Neural Architecture Search} by Thomas, Jan Hendrik and Frank. 2) For the second finding of the search space and the performance of a randomly sampled architecture, existing work from Facebook AI Research group has studied this. And the existing work gives more experiments and discussion than this submission (from my own perspective). https://arxiv.org/pdf/1904.01569.pdf 3) The conducted experiments in this submission also have certain risks to support the claims/conclusions of it. For example, only datasets of image classification are adopted. Another factor is the hyper-parameter tuning (actually, the authors also mention this in the last paragraph on Page 4). All the compared methods, either NAS methods or random sample baseline, should receive the same training procedure to get a fair experimental comparison. The above mentioned existing work makes the contributions of this submission less, and the experimental results may not be convincing enough. These lead to a reject. However, a great point is made by the authors in the last paragraph of Section 6: hyperparameter of NAS methods should be either stable enough or counted toward the cost.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
NAS evaluation is frustratingly hard
Neural Architecture Search (NAS) is an exciting new field which promises to be as much as a game-changer as Convolutional Neural Networks were in 2012. Despite many great works leading to substantial improvements on a variety of tasks, comparison between different methods is still very much an open issue. While most algorithms are tested on the same datasets, there is no shared experimental protocol followed by all. As such, and due to the under-use of ablation studies, there is a lack of clarity regarding why certain methods are more effective than others. Our first contribution is a benchmark of 8 NAS methods on 5 datasets. To overcome the hurdle of comparing methods with different search spaces, we propose using a method’s relative improvement over the randomly sampled average architecture, which effectively removes advantages arising from expertly engineered search spaces or training protocols. Surprisingly, we find that many NAS techniques struggle to significantly beat the average architecture baseline. We perform further experiments with the commonly used DARTS search space in order to understand the contribution of each component in the NAS pipeline. These experiments highlight that: (i) the use of tricks in the evaluation protocol has a predominant impact on the reported performance of architectures; (ii) the cell-based search space has a very narrow accuracy range, such that the seed has a considerable impact on architecture rankings; (iii) the hand-designed macrostructure (cells) is more important than the searched micro-structure (operations); and (iv) the depth-gap is a real phenomenon, evidenced by the change in rankings between 8 and 20 cell architectures. To conclude, we suggest best practices, that we hope will prove useful for the community and help mitigate current NAS pitfalls, e.g. difficulties in reproducibility and comparison of search methods. We provide the code used for our experiments at link-to-come.
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>



In this submission, the authors conduct a series of experiments on five image classification datasets to compare several existing NAS methods. Based on these experimental results, they point out: 1) how a network is trained (i.e., training protocols/tricks such as DropPath, Cutout) plays an important role for the final accuracy; 2) within the search space, the existing NAS methods perform close to or slightly better than a random sampling baseline; 3) hyperparameters of NAS methods also have significant effect on the performance. With these interesting findings, I suggest rejecting this submission. The reasons are as follows: 1) For the first finding of training protocol, several existing papers and books already discussed it, such as Li & Talwalkar (2019) and the book chapter {Neural Architecture Search} by Thomas, Jan Hendrik and Frank. 2) For the second finding of the search space and the performance of a randomly sampled architecture, existing work from Facebook AI Research group has studied this. And the existing work gives more experiments and discussion than this submission (from my own perspective). https://arxiv.org/pdf/1904.01569.pdf 3) The conducted experiments in this submission also have certain risks to support the claims/conclusions of it. For example, only datasets of image classification are adopted. Another factor is the hyper-parameter tuning (actually, the authors also mention this in the last paragraph on Page 4). All the compared methods, either NAS methods or random sample baseline, should receive the same training procedure to get a fair experimental comparison. The above mentioned existing work makes the contributions of this submission less, and the experimental results may not be convincing enough. These lead to a reject. However, a great point is made by the authors in the last paragraph of Section 6: hyperparameter of NAS methods should be either stable enough or counted toward the cost.
assistant
In this submission, the authors conduct a series of experiments on five image classification datasets to compare several existing NAS methods. Based on these experimental results, they point out: 1) how a network is trained (i.e., training protocols/tricks such as DropPath, Cutout) plays an important role for the final accuracy; 2) within the search space, the existing NAS methods perform close to or slightly better than a random sampling baseline; 3) hyperparameters of NAS methods also have significant effect on the performance. With these interesting findings, I suggest rejecting this submission. The reasons are as follows: 1) For the first finding of training protocol, several existing papers and books already discussed it, such as Li & Talwalkar (2019) and the book chapter {Neural Architecture Search} by Thomas, Jan Hendrik and Frank. 2) For the second finding of the search space and the performance of a randomly sampled architecture, existing work from Facebook AI Research group has studied this. And the existing work gives more experiments and discussion than this submission (from my own perspective). https://arxiv.org/pdf/1904.01569.pdf 3) The conducted experiments in this submission also have certain risks to support the claims/conclusions of it. For example, only datasets of image classification are adopted. Another factor is the hyper-parameter tuning (actually, the authors also mention this in the last paragraph on Page 4). All the compared methods, either NAS methods or random sample baseline, should receive the same training procedure to get a fair experimental comparison. The above mentioned existing work makes the contributions of this submission less, and the experimental results may not be convincing enough. These lead to a reject. However, a great point is made by the authors in the last paragraph of Section 6: hyperparameter of NAS methods should be either stable enough or counted toward the cost.