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A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Search

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arxiv 2204.03916 v2 pith:BSGNOW3L submitted 2022-04-08 cs.CV cs.LG

Stephen Cha , Taehyeon Kim , Hayeon Lee , Se-Young Yun This is my paper · ORCID
classification cs.CVcs.LG
keywords optimizationsupernetarchitecturemethodssearchspatialsurveytemporal
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This survey focuses on categorizing and evaluating the methods of supernet optimization in the field of Neural Architecture Search (NAS). Supernet optimization involves training a single, over-parameterized network that encompasses the search space of all possible network architectures. The survey analyses supernet optimization methods based on their approaches to spatial and temporal optimization. Spatial optimization relates to optimizing the architecture and parameters of the supernet and its subnets, while temporal optimization deals with improving the efficiency of selecting architectures from the supernet. The benefits, limitations, and potential applications of these methods in various tasks and settings, including transferability, domain generalization, and Transformer models, are also discussed.

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Cited by 2 Pith papers

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  1. Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration

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    LAASP prunes neural networks during training by greedily selecting the best layer and filter-importance criterion at each step using the network's loss on a data subset.

  2. Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts

    cs.LG 2026-07 conditional novelty 5.0 of 10

    Across four NAS/DNN-predictor regression benchmarks, GEN (deep graph convolution) achieves the best average rank over 11 GNN message-passing layers, though attention GATv2 wins on the largest graphs.

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