# MixPath: A Unified Approach for One-shot Neural Architecture Search

Keywords:

differentiable architecture searchBatch Normalizationneural architecture searcharchitecture searchweight sharingMore(9+)

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Abstract:

The expressiveness of search space is a key concern in neural architecture search (NAS). Previous block-level approaches mainly focus on searching networks that chain one operation after another. Incorporating multi-path search space with the one-shot doctrine remains untackled. In this paper, we investigate the supernet behavior under ...More

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Introduction

- Complete automation in neural network design is one of the most important research directions of automated machine learning [26,37].
- It poses a challenge to think of its one-shot counterpart, i.e., to train a one-shot supernet that can accurately predict the performance of its multi-path submodels.
- FairNAS [6] resolves the ranking difficulty in the single-path case with a fairness strategy, it is inherently difficult to apply the same method in the multi-path scenario.
- The vanilla training of multi-path supernet can’t provide a confident ranking.
- The authors dive into its real causes and undertake a unified

Highlights

- Complete automation in neural network design is one of the most important research directions of automated machine learning [26,37]
- One-shot approaches [1, 6, 12, 21] make use of weight-sharing mechanism that reduces a large amount of computational cost, but these approaches mainly focus on searching for single-path networks
- – We disclose why vanilla multi-path training could fail, for which we propose a novel yet lightweight mechanism, called shadow batch normalization, (SBN, see Fig. 1), to stabilize the supernet with a neglectable cost
- To prove that SBN can stabilize the supernet training and improve its ranking, we score our method on a subset of a common benchmark NAS-Bench-101 [47], where the model is stacked by 9 cells, each cell has at most 5 internal nodes
- We propose a unified approach for one-shot neural architecture search, which bridges the gap between one-shot methodology and multi-path search space
- Our future work regards how to further improve the evaluation performance of the supernet, and to provide a deeper theoretical analysis between the weight-sharing mechanism and ranking ability

Methods

- 4.1 Confirmatory Experiments on NAS-Bench-101

To prove that SBN can stabilize the supernet training and improve its ranking, the authors score the method on a subset of a common benchmark NAS-Bench-101 [47], where the model is stacked by 9 cells, each cell has at most 5 internal nodes. - 4.1 Confirmatory Experiments on NAS-Bench-101.
- To prove that SBN can stabilize the supernet training and improve its ranking, the authors score the method on a subset of a common benchmark NAS-Bench-101 [47], where the model is stacked by 9 cells, each cell has at most 5 internal nodes.
- The outputs of selected paths are summed up to give an input to the fifth node, after which the proposed SBNs are used.

Results

- With SBN enabled, the best model found obtains 97.35% top-1 accuracy, higher than the best 97.12% when SBN is disabled.
- The authors' future work regards how to further improve the evaluation performance of the supernet, and to provide a deeper theoretical analysis between the weight-sharing mechanism and ranking ability

Conclusion

- The authors propose a unified approach for one-shot neural architecture search, which bridges the gap between one-shot methodology and multi-path search space.
- Existing single-path approaches can be regarded as a special case of ours.
- The proposed method uses SBNs to catch the changing features from various branch combinations, which successfully solves two difficulties of vanilla multi-path adaptation: the unstable training of supernet and the unbearable weakness of model ranking.
- The authors' future work regards how to further improve the evaluation performance of the supernet, and to provide a deeper theoretical analysis between the weight-sharing mechanism and ranking ability

Summary

## Introduction:

Complete automation in neural network design is one of the most important research directions of automated machine learning [26,37].- It poses a challenge to think of its one-shot counterpart, i.e., to train a one-shot supernet that can accurately predict the performance of its multi-path submodels.
- FairNAS [6] resolves the ranking difficulty in the single-path case with a fairness strategy, it is inherently difficult to apply the same method in the multi-path scenario.
- The vanilla training of multi-path supernet can’t provide a confident ranking.
- The authors dive into its real causes and undertake a unified
## Objectives:

The authors' objectives are to maximize the classification accuracy while minimizing the FLOPS.- The authors' goal is to find the best models under 500M FLOPS
## Methods:

4.1 Confirmatory Experiments on NAS-Bench-101

To prove that SBN can stabilize the supernet training and improve its ranking, the authors score the method on a subset of a common benchmark NAS-Bench-101 [47], where the model is stacked by 9 cells, each cell has at most 5 internal nodes.- 4.1 Confirmatory Experiments on NAS-Bench-101.
- To prove that SBN can stabilize the supernet training and improve its ranking, the authors score the method on a subset of a common benchmark NAS-Bench-101 [47], where the model is stacked by 9 cells, each cell has at most 5 internal nodes.
- The outputs of selected paths are summed up to give an input to the fifth node, after which the proposed SBNs are used.
## Results:

With SBN enabled, the best model found obtains 97.35% top-1 accuracy, higher than the best 97.12% when SBN is disabled.- The authors' future work regards how to further improve the evaluation performance of the supernet, and to provide a deeper theoretical analysis between the weight-sharing mechanism and ranking ability
## Conclusion:

The authors propose a unified approach for one-shot neural architecture search, which bridges the gap between one-shot methodology and multi-path search space.- Existing single-path approaches can be regarded as a special case of ours.
- The proposed method uses SBNs to catch the changing features from various branch combinations, which successfully solves two difficulties of vanilla multi-path adaptation: the unstable training of supernet and the unbearable weakness of model ranking.

- Table1: Comparison of Kendall Taus between MixPath supernets (m = 4) trained with SBNs and vanilla BNs, based on 70 sampled models from NAS-Bench-101 [<a class="ref-link" id="c44" href="#r44">44</a>]. Each control group is repeated 3 times on different seeds. †: after BN calibration
- Table2: Comparison of architectures on CIFAR-10. †: MultAdds computed using the genotypes provided by the authors. : transfered from ImageNet
- Table3: Comparison with state-of-the-art models on ImageNet
- Table4: Object detection result of various drop-in backbones on the COCO dataset
- Table5: Comparison of Kendall Taus between MixPath supernets (m = 3) trained with SBNs and vanilla BNs based on 70 sampled models from NAS-Bench-101 [<a class="ref-link" id="c44" href="#r44">44</a>]. Each control group is repeated 3 times on different seeds τ

Related work

- Model Ranking Correlation. The most difficult and costly procedure for neural architecture search is the evaluation of any candidate model. To this end, various proxies [37, 50, 51], explicit or implicit performance predictors [25, 27] are developed to avoid the intractable evaluation. Recent one-shot approaches [1, 6, 12] utilize a supernet where each submodel can be rapidly assessed with inherited weights. It should be emphasized that the ranking ability for this family of algorithms is of the uttermost importance [1], whose sole purpose is to evaluate networks. To quantitatively analyze their ranking ability, previous works like [6, 22, 47, 49] have applied a Kendall Tau measure [18].

Funding

- As a result, with SBN enabled, the best model found obtains 97.35% top-1 accuracy, higher than the best 97.12% when SBN is disabled
- We also achieved state-of-the-art architectures like MixPath-A (76.9%) and B (77.2%) on ImageNet
- Our future work regards how to further improve the evaluation performance of the supernet, and to provide a deeper theoretical analysis between the weight-sharing mechanism and ranking ability

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