Automating the research for the best neural network model is a task that has
gained more and more relevance in the last few years. In this context, Neural
Architecture Search (NAS) represents the most effective technique whose results
rival the state of the art hand-crafted architectures. However, this approach
requires a lot of computational capabilities as well as research time, which
makes prohibitive its usage in many real-world scenarios. With its sequential
model-based optimization strategy, Progressive Neural Architecture Search
(PNAS) represents a possible step forward to face this resources issue. Despite
the quality of the found network architectures, this technique is still limited
in research time. A significant step in this direction has been done by
Pareto-Optimal Progressive Neural Architecture Search (POPNAS), which expands
PNAS with a time predictor to enable a trade-off between search time and
accuracy, considering a multi-objective optimization problem. This paper
proposes a new version of the Pareto-Optimal Progressive Neural Architecture
Search, called POPNASv2. Our approach enhances its first version and improves
its performance. We expanded the search space by adding new operators and
improved the quality of both predictors to build more accurate Pareto fronts.
Moreover, we introduced cell equivalence checks and enriched the search
strategy with an adaptive greedy exploration step. Our efforts allow POPNASv2
to achieve PNAS-like performance with an average 4x factor search time
speed-up.

本文介绍了 Pareto-Optimal Progressive Neural Architecture Search (POPNASv2) 方法，该方法在原有的基础上进行了改进，通过加入新操作符、改进预测模型、引入细胞等价检查机制以及自适应贪心探索策略等方法，实现了 PNAS 相似的性能，同时平均搜索时间快了 4 倍。