Summary of Hep-nas: Towards Efficient Few-shot Neural Architecture Search Via Hierarchical Edge Partitioning, by Jianfeng Li et al.
HEP-NAS: Towards Efficient Few-shot Neural Architecture Search via Hierarchical Edge Partitioning
by Jianfeng Li, Jiawen Zhang, Feng Wang, Lianbo Ma
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty Summary: One-shot neural architecture search (NAS) has advanced significantly by adopting weight-sharing strategies, but the accuracy of performance estimation can be compromised by co-adaptation. Few-shot methods divide the supernet into sub-supernets, neglecting relationships among edges and resulting in performance degradation on large search spaces. HEP-NAS is a hierarchy-wise partition algorithm that treats shared edge nodes as hierarchies, permuting and splitting edges to directly search for optimal operation combinations. This approach aligns with NAS goals. HEP-NAS selects the most promising sub-supernet after segmentation, narrowing the search space. To improve performance evaluation, it employs search space mutual distillation, stabilizing training and accelerating convergence. Experimental results demonstrate HEP-NAS’ superiority over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper is about finding the best way to design neural networks using a new method called HEP-NAS. Neural architecture search (NAS) is important because it helps us create better artificial intelligence models. However, some NAS methods have limitations that make them less effective. HEP-NAS addresses these issues by grouping similar edges together and searching for the best combination of operations. This approach allows us to find better neural networks within a given budget. The results show that HEP-NAS performs better than other state-of-the-art methods. |
Keywords
» Artificial intelligence » Distillation » Few shot » One shot
