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Neural Architecture Search (NAS) automates and prospers the design of neuralnetworks. Estimator-based NAS has been proposed recently to model therelationship between architectures and their performance to enable scalable andflexible search. However, existing estimator-based methods encode thearchitecture into a latent space without considering graph similarity. Ignoringgraph similarity in node-based search space may induce a large inconsistencybetween similar graphs and their distance in the continuous encoding space,leading to inaccurate encoding representation and/or reduced representationcapacity that can yield sub-optimal search results. To preserve graphcorrelation information in encoding, we propose NASGEM which stands for NeuralArchitecture Search via Graph Embedding Method. NASGEM is driven by a novelgraph embedding method equipped with similarity measures to capture the graphtopology information. By precisely estimating the graph distance and using anauxiliary Weisfeiler-Lehman kernel to guide the encoding, NASGEM can utilizeadditional structural information to get more accurate graph representation toimprove the search efficiency. GEMNet, a set of networks discovered by NASGEM,consistently outperforms networks crafted by existing search methods inclassification tasks, i.e., with 0.4%-3.6% higher accuracy while having 11%-21% fewer Multiply-Accumulates. We further transfer GEMNet for COCO objectdetection. In both one-stage and twostage detectors, our GEMNet surpasses itsmanually-crafted and automatically-searched counterparts.

NASGEM: Neural Architecture Search via Graph Embedding Method