Name
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 Levy 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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-61609-0_25 | ICANN (1) |
DocType | Citations | PageRank |
Conference | 1 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nan Zhang | 1 | 1 | 1.70 |
| Jianzong Wang | 2 | 61 | 34.65 |
| Jian Yang | 3 | 1 | 0.34 |
| Xiaoyang Qu | 4 | 1 | 0.68 |
| jing xiao | 5 | 80 | 42.68 |