Multi-objective Cuckoo Algorithm for Mobile Devices Network Architecture Search

Nan Zhang, Jianzong Wang, Jian Yang, Xiaoyang Qu, Jing Xiao

Abstract: The network architecture search technique is nowadays becoming the next generation paradigm of architectural engineering, which could free experts from trials and errors while achieving state-of-the-art performances in lots of applications such as image classification and language modeling. It is immensely crucial for deploying deep networks on a wide range of mobile devices with limited computing resources to provide more flexible service. In this paper, a novel multi-objective oriented algorithm called MOCS-Net for mobile devices network architecture search is proposed. In particular, the search space is compact and flexible which leverages good virtues from efficient mobile CNNs and is block-wise constructed by different stacked blocks. Moreover, an enhanced multi-objective cuckoo algorithm is incorporated, in which mutation is achieved by Lévy flights which are performed at the block level. Experimental results suggest that MOCS-Net could find competitive neural architectures on ImageNet with a better trade-off among various competing objectives compared with other state-of-the-art methods. Meanwhile, these results show the effectiveness of proposed MOCS-Net and the promise to further the use of MOCS-Net in various deep-learning paradigms.