HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking

Abstract

The use of automatic methods, often referred to as Neural Architecture Search (NAS), in designing neural network architectures has recently drawn considerable attention. In this work, we present an efficient NAS approach, named HM- NAS, that generalizes existing weight sharing based NAS approaches. Existing weight sharing based NAS approaches still adopt hand-designed heuristics to generate architecture candidates. As a consequence, the space of architecture candidates is constrained in a subset of all possible architectures, making the architecture search results sub-optimal. HM-NAS addresses this limitation via two innovations. First, HM-NAS incorporates a multi-level architecture encoding scheme to enable searching for more flexible network architectures. Second, it discards the hand-designed heuristics and incorporates a hierarchical masking scheme that automatically learns and determines the optimal architecture. Compared to state-of-the-art weight sharing based approaches, HM-NAS is able to achieve better architecture search performance and competitive model evaluation accuracy. Without the constraint imposed by the hand-designed heuristics, our searched networks contain more flexible and meaningful architectures that existing weight sharing based NAS approaches are not able to discover.

arXiv comment9 pages, 6 figures, 6 tables. Nominated for ICCV 2019 Neural Architects Workshop Best Paper Award

References in corpus (10)

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Improved Regularization of Convolutional Neural Networks with Cutout
• DARTS: Differentiable Architecture Search
• Binarized Neural Networks
• Neural Architecture Optimization
• Designing Neural Network Architectures using Reinforcement Learning
• Random Search and Reproducibility for Neural Architecture Search
• Single Path One-Shot Neural Architecture Search with Uniform Sampling
• DetNAS: Backbone Search for Object Detection
• Evaluating the Search Phase of Neural Architecture Search

Cited by in corpus (1)

• BETANAS: BalancEd TrAining and selective drop for Neural Architecture Search