Stabilizing DARTS with Amended Gradient Estimation on Architectural Parameters
Differentiable neural architecture search has been a popular methodology of exploring architectures for deep learning. Despite the great advantage of search efficiency, it often suffers weak stability, which obstacles it from being applied to a large search space or being flexibly adjusted to different scenarios. This paper investigates DARTS, the currently most popular differentiable search algorithm, and points out an important factor of instability, which lies in its approximation on the gradients of architectural parameters. In the current status, the optimization algorithm can converge to another point which results in dramatic inaccuracy in the re-training process. Based on this analysis, we propose an amending term for computing architectural gradients by making use of a direct property of the optimality of network parameter optimization. Our approach mathematically guarantees that gradient estimation follows a roughly correct direction, which leads the search stage to converge on reasonable architectures. In practice, our algorithm is easily implemented and added to DARTS-based approaches efficiently. Experiments on CIFAR and ImageNet demonstrate that our approach enjoys accuracy gain and, more importantly, enables DARTS-based approaches to explore much larger search spaces that have not been studied before.
This paper offers through analysis on instability of NAS training that the system degenerates and reaches to trivial solutions, e.g., more skip-connect operators, as training goes longer. Motivated by this issue, this paper proposes an approach to stabilize NAS training. Strength: [1] Theoretical analysis [2] The paper is well written [3] This paper is trying to solve a very interesting and important problem Weakness: [1] Lack of ablation study. It would be better to show learning curve like Fig.1 to illustrate how the proposed approach helps to stabilize training and how about >500 epoch, any unstable? [2]The proposed approach offers comparable results with SOTA (early stopping). I agree that it opens a direction of stable NAS training, but the contribution so far is limited. I expect to see quality gains due to improved training technology
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Stabilizing DARTS with Amended Gradient Estimation on Architectural Parameters
Differentiable neural architecture search has been a popular methodology of exploring architectures for deep learning. Despite the great advantage of search efficiency, it often suffers weak stability, which obstacles it from being applied to a large search space or being flexibly adjusted to different scenarios. This paper investigates DARTS, the currently most popular differentiable search algorithm, and points out an important factor of instability, which lies in its approximation on the gradients of architectural parameters. In the current status, the optimization algorithm can converge to another point which results in dramatic inaccuracy in the re-training process. Based on this analysis, we propose an amending term for computing architectural gradients by making use of a direct property of the optimality of network parameter optimization. Our approach mathematically guarantees that gradient estimation follows a roughly correct direction, which leads the search stage to converge on reasonable architectures. In practice, our algorithm is easily implemented and added to DARTS-based approaches efficiently. Experiments on CIFAR and ImageNet demonstrate that our approach enjoys accuracy gain and, more importantly, enables DARTS-based approaches to explore much larger search spaces that have not been studied before.
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 offers through analysis on instability of NAS training that the system degenerates and reaches to trivial solutions, e.g., more skip-connect operators, as training goes longer. Motivated by this issue, this paper proposes an approach to stabilize NAS training. Strength: [1] Theoretical analysis [2] The paper is well written [3] This paper is trying to solve a very interesting and important problem Weakness: [1] Lack of ablation study. It would be better to show learning curve like Fig.1 to illustrate how the proposed approach helps to stabilize training and how about >500 epoch, any unstable? [2]The proposed approach offers comparable results with SOTA (early stopping). I agree that it opens a direction of stable NAS training, but the contribution so far is limited. I expect to see quality gains due to improved training technology
assistant
This paper offers through analysis on instability of NAS training that the system degenerates and reaches to trivial solutions, e.g., more skip-connect operators, as training goes longer. Motivated by this issue, this paper proposes an approach to stabilize NAS training. Strength: [1] Theoretical analysis [2] The paper is well written [3] This paper is trying to solve a very interesting and important problem Weakness: [1] Lack of ablation study. It would be better to show learning curve like Fig.1 to illustrate how the proposed approach helps to stabilize training and how about >500 epoch, any unstable? [2]The proposed approach offers comparable results with SOTA (early stopping). I agree that it opens a direction of stable NAS training, but the contribution so far is limited. I expect to see quality gains due to improved training technology