ASAP: Architecture Search, Anneal and Prune Asaf Noy1, Niv Nayman1, Tal Ridnik1, Nadav Zamir1, Sivan Doveh2, Itamar Friedman1, Raja Giryes2, and Lihi Zelnik-Manor1 1DAMO Academy, Alibaba Group (fi
[email protected]) 2School of Electrical Engineering, Tel-Aviv University Tel-Aviv, Israel Abstract methods, such as ENAS [34] and DARTS [12], reduced the search time to a few GPU days, while not compromising Automatic methods for Neural Architecture Search much on accuracy. While this is a major advancement, there (NAS) have been shown to produce state-of-the-art net- is still a need to speed up the process to make the automatic work models. Yet, their main drawback is the computa- search affordable and applicable to more problems. tional complexity of the search process. As some primal In this paper we propose an approach that further re- methods optimized over a discrete search space, thousands duces the search time to a few hours, rather than days or of days of GPU were required for convergence. A recent weeks. The key idea that enables this speed-up is relaxing approach is based on constructing a differentiable search the discrete search space to be continuous, differentiable, space that enables gradient-based optimization, which re- and annealable. For continuity and differentiability, we fol- duces the search time to a few days. While successful, it low the approach in DARTS [12], which shows that a con- still includes some noncontinuous steps, e.g., the pruning of tinuous and differentiable search space allows for gradient- many weak connections at once. In this paper, we propose a based optimization, resulting in orders of magnitude fewer differentiable search space that allows the annealing of ar- computations in comparison with black-box search, e.g., chitecture weights, while gradually pruning inferior opera- of [48, 35].