ParZC: Parametric Zero-Cost Proxies for Efficient NAS

Recent advancements in Zero-shot Neural Architecture Search (NAS) highlight the ability of zero-cost proxies in identifying superior architecture. However, we identify a critical issue with current zero-cost proxies: they aggregate node-wise zero-cost statistics without considering that not all nodes in a neural network equally impact performance estimation. Our observations reveal that node-wise zero-cost statistics significantly vary in their contributions to performance, with each node exhibiting a degree of uncertainty. Based on this insight, we introduce a novel method called Parametric Zero-Cost Proxies (ParZC) framework to enhance the adaptability of zero-cost proxies through parameterization. To address the node indiscrimination, we propose a Mixer Architecture with Bayesian Network (MABN) to explore the node-wise zero-cost statistics and estimate node-specific uncertainty. Moreover, we propose DiffKendall as a loss function to improve ranking consistency. Comprehensive experiments on NAS-Bench-101, 201, and NDS demonstrate the superiority of our proposed ParZC compared to existing zero-shot NAS methods. Additionally, we demonstrate the versatility and adaptability of ParZC on Vision Transformer search space.

Authors

• Peijie Dong The Hong Kong University of Science and Technology (Guang Zhou)
• Lujun Li The Hong Kong University of Science and Technology
• Zhenheng Tang The Hong Kong University of Science and Technology
• Xiang Liu Southeast University The Chinese University of Hong Kong
• Zimian Wei National University of Defense Technology
• Qiang Wang
• Xiaowen Chu The Hong Kong University of Science and Technology (Guangzhou)

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