# Exploiting Operation Importance for Differentiable Neural Architecture Search

arXiv: https://arxiv.org/abs/1911.10511 · HTML: https://mlsanity.com/paper/1911.10511/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2019.

## Built on by (most cited first, 11 of 11)

- [Shapley-NAS: Discovering Operation Contribution for Neural Architecture Search](https://mlsanity.com/paper/2206.09811/) (2022)
- [A Survey on Computationally Efficient Neural Architecture Search](https://mlsanity.com/paper/2206.01520/) (2022)
- [Partial Connection Based on Channel Attention for Differentiable Neural Architecture Search](https://mlsanity.com/paper/2208.00791/) (2022)
- [Full-attention based Neural Architecture Search using Context Auto-regression](https://mlsanity.com/paper/2111.07139/) (2021)
- [Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search](https://mlsanity.com/paper/2307.04429/) (2023)
- [Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture](https://mlsanity.com/paper/2306.11982/) (2023)
- [Where and How to Enhance: Discovering Bit-Width Contribution for Mixed Precision Quantization](https://mlsanity.com/paper/2508.03002/) (2025)
- [Exploring Complicated Search Spaces with Interleaving-Free Sampling](https://mlsanity.com/paper/2112.02488/) (2021)
- [Searching for the Fakes: Efficient Neural Architecture Search for General Face Forgery Detection](https://mlsanity.com/paper/2306.08830/) (2023)
- [FX-DARTS: Designing Topology-unconstrained Architectures with Differentiable Architecture Search and Entropy-based Super-network Shrinking](https://mlsanity.com/paper/2504.20079/) (2025)
- [DNAD: Differentiable Neural Architecture Distillation](https://mlsanity.com/paper/2504.20080/) (2025)

## Abstract (arXiv)

Recently, differentiable neural architecture search methods significantly reduce the search cost by constructing a super network and relax the architecture representation by assigning architecture weights to the candidate operations. All the existing methods determine the importance of each operation directly by architecture weights. However, architecture weights cannot accurately reflect the importance of each operation; that is, the operation with the highest weight might not related to the best performance. To alleviate this deficiency, we propose a simple yet effective solution to neural architecture search, termed as exploiting operation importance for effective neural architecture search (EoiNAS), in which a new indicator is proposed to fully exploit the operation importance and guide the model search. Based on this new indicator, we propose a gradual operation pruning strategy to further improve the search efficiency and accuracy. Experimental results have demonstrated the effectiveness of the proposed method. Specifically, we achieve an error rate of 2.50% on CIFAR-10, which significantly outperforms state-of-the-art methods. When transferred to ImageNet, it achieves the top-1 error of 25.6%, comparable to the state-of-the-art performance under the mobile setting.