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Zero-Shot Neural Architecture Search: Challenges, Solutions, and Opportunities
Summary
Zero-shot Neural Architecture Search (NAS) uses accuracy predictors (proxies) to avoid costly training. This review compares state-of-the-art zero-shot NAS methods, focusing on hardware awareness and effectiveness.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Neural Architecture Search (NAS) is computationally expensive due to training requirements.
- Zero-shot NAS methods aim to predict network performance without parameter training.
- Existing zero-shot proxies are inspired by deep learning theory and show promise.
Purpose of the Study:
- To comprehensively review and compare state-of-the-art zero-shot NAS approaches.
- To emphasize the hardware awareness of these zero-shot NAS methods.
- To identify potential improvements for future proxy designs.
Main Methods:
- Review of mainstream zero-shot NAS proxies and their theoretical foundations.
- Large-scale experimental comparison of zero-shot proxies.
- Evaluation in both hardware-aware and hardware-oblivious NAS settings.
Main Results:
- Zero-shot NAS proxies can effectively predict network accuracy without training.
- Demonstrated effectiveness of reviewed proxies in diverse NAS scenarios.
- Highlighted the importance and impact of hardware awareness in zero-shot NAS.
Conclusions:
- Zero-shot NAS offers a viable, efficient alternative to traditional NAS.
- Further research into designing improved zero-shot proxies is warranted.
- Hardware-aware proxy design is crucial for practical NAS applications.
