Summary of Az-nas: Assembling Zero-cost Proxies For Network Architecture Search, by Junghyup Lee et al.
AZ-NAS: Assembling Zero-Cost Proxies for Network Architecture Search
by Junghyup Lee, Bumsub Ham
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The proposed AZ-NAS approach leverages an ensemble of zero-cost proxies to enhance the correlation between predicted network rankings and ground truth performance. This is achieved by introducing four novel proxies that analyze distinct traits of architectures in terms of expressivity, progressivity, trainability, and complexity. The proxy scores can be obtained simultaneously within a single forward and backward pass, making the overall NAS process highly efficient. A non-linear ranking aggregation method integrates the predicted rankings effectively, highlighting networks consistently ranked high across all proxies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AZ-NAS is a new way to find good network architectures without training them. It uses special tools (proxies) that help predict which networks will perform well. These proxies are like a report card for each network, and they look at different things like how creative the network is or how easy it is to train. The best part is that these proxy scores can be calculated quickly, making the whole process fast and efficient. By combining these scores in a special way, AZ-NAS can pick out the top-performing networks. |
