TG-NAS: Leveraging Zero-Cost Proxies with Transformer and Graph Convolution Networks for Efficient Neural Architecture Search
<|reference_start|>TG-NAS: Leveraging Zero-Cost Proxies with Transformer and Graph Convolution Networks for Efficient Neural Architecture Search: Neural architecture search (NAS) is an effective method for discovering new convolutional neural network (CNN) architectures. However, existing approaches often require time-consuming training or intensive sampling and evaluations. Zero-shot NAS aims to create training-free proxies for architecture performance prediction. However, existing proxies have suboptimal performance, and are often outperformed by simple metrics such as model parameter counts or the number of floating-point operations. Besides, existing model-based proxies cannot be generalized to new search spaces with unseen new types of operators without golden accuracy truth. A universally optimal proxy remains elusive. We introduce TG-NAS, a novel model-based universal proxy that leverages a transformer-based operator embedding generator and a graph convolution network (GCN) to predict architecture performance. This approach guides neural architecture search across any given search space without the need of retraining. Distinct from other model-based predictor subroutines, TG-NAS itself acts as a zero-cost (ZC) proxy, guiding architecture search with advantages in terms of data independence, cost-effectiveness, and consistency across diverse search spaces. Our experiments showcase its advantages over existing proxies across various NAS benchmarks, suggesting its potential as a foundational element for efficient architecture search. TG-NAS achieves up to 300X improvements in search efficiency compared to previous SOTA ZC proxy methods. Notably, it discovers competitive models with 93.75% CIFAR-10 accuracy on the NAS-Bench-201 space and 74.5% ImageNet top-1 accuracy on the DARTS space.<|reference_end|>
@article{qiao2024tg-nas:,
    title={TG-NAS: Leveraging Zero-Cost Proxies with Transformer and Graph
  Convolution Networks for Efficient Neural Architecture Search},
    author={Ye Qiao, Haocheng Xu, and Sitao Huang},
    journal={arXiv preprint arXiv:2404.00271},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00271},
    primaryClass={cs.LG cs.AI}
}