GRASP-GCN: Graph-Shape Prioritization for Neural Architecture Search under Distribution Shifts
<|reference_start|>GRASP-GCN: Graph-Shape Prioritization for Neural Architecture Search under Distribution Shifts: Neural Architecture Search (NAS) methods have shown to output networks that largely outperform human-designed networks. However, conventional NAS methods have mostly tackled the single dataset scenario, incuring in a large computational cost as the procedure has to be run from scratch for every new dataset. In this work, we focus on predictor-based algorithms and propose a simple and efficient way of improving their prediction performance when dealing with data distribution shifts. We exploit the Kronecker-product on the randomly wired search-space and create a small NAS benchmark composed of networks trained over four different datasets. To improve the generalization abilities, we propose GRASP-GCN, a ranking Graph Convolutional Network that takes as additional input the shape of the layers of the neural networks. GRASP-GCN is trained with the not-at-convergence accuracies, and improves the state-of-the-art of 3.3 % for Cifar-10 and increasing moreover the generalization abilities under data distribution shift.<|reference_end|>
@article{casarin2024grasp-gcn:,
    title={GRASP-GCN: Graph-Shape Prioritization for Neural Architecture Search
  under Distribution Shifts},
    author={Sofia Casarin, Oswald Lanz, Sergio Escalera},
    journal={arXiv preprint arXiv:2405.06994},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06994},
    primaryClass={cs.CV cs.LG}
}