Picking up the pieces: separately evaluating supernet training and architecture selection

Gabriel Meyer-Lee, Nick Cheney

Keywords: neural architecture search, automl, deep learning theory

Abstract: Differentiable Neural Architecture Search (NAS) has emerged as a simple and efficient method for the automated design of neural networks. Recent research has demonstrated improvements on various aspects on the original algorithm (DARTS), but comparative evaluation of these advances remains costly and difficult. We frame supernet NAS as a two-stage search, decoupling the training of the supernet from the extraction of a final design from the supernet. We propose a set of metrics which utilize benchmark data sets to evaluate each stage of the search process independently. We demonstrate two metrics measuring separately the quality of the supernet's shared weights and the quality of the learned sampling distribution, as well as corresponding statistics approximating the reliance of the second stage search on these components of the supernet. These metrics facilitate both more robust evaluation of NAS algorithms and provide practical method for designing complete NAS algorithms from separate supernet training and architecture selection techniques.

One-sentence Summary: A 2-stage framework for evaluating differentiable supernet NAS methods with evaluation statistics for each search stage.