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Common Neural Architecture Search methods generate large amounts of candidatearchitectures that need training in order to assess their performance and findan optimal architecture. To minimize the search time we use differentperformance estimation strategies. The effectiveness of such strategies variesin terms of accuracy and fit and query time. This study proposes a new method,EmProx Score (Embedding Proximity Score). Similar to Neural ArchitectureOptimization (NAO), this method maps candidate architectures to a continuousembedding space using an encoder-decoder framework. The performance ofcandidates is then estimated using weighted kNN based on the embedding vectorsof architectures of which the performance is known. Performance estimations ofthis method are comparable to the MLP performance predictor used in NAO interms of accuracy, while being nearly nine times faster to train compared toNAO. Benchmarking against other performance estimation strategies currentlyused shows similar to better accuracy, while being five up to eighty timesfaster.

EmProx: Neural Network Performance Estimation For Neural Architecture Search