Paper Info
Title
AGNAS: Attention-Guided Micro and Macro-Architecture Search.
Abstract
Micro- and macro-architecture search have emerged as two popular NAS paradigms recently. Existing methods leverage different search strategies for searching micro- and macro- architectures. When using architecture parameters to search for micro-structure such as normal cell and reduction cell, the architecture parameters can not fully reflect the corresponding operation importance. When searching for the macro-structure chained by pre-defined blocks, many sub-networks need to be sampled for evaluation, which is very time-consuming. To address the two issues, we propose a new search paradigm, that is, leverage the attention mechanism to guide the micro- and macro-architecture search, namely AGNAS. Specifically, we introduce an attention module and plug it behind each candidate operation or each candidate block. We utilize the attention weights to represent the importance of the relevant operations for the micro search or the importance of the relevant blocks for the macro search. Experimental results show that AGNAS can achieve 2.46% test error on CIFAR-10 in the DARTS search space, and 23.4% test error when directly searching on ImageNet in the ProxylessNAS search space. AGNAS also achieves optimal performance on NAS-Bench-201, outperforming state-of-the-art approaches. The source code can be available at https://github.com/Sunzh1996/AGNAS.
Year
Venue
DocType
2022
International Conference on Machine Learning
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Zihao Sun101.01
Yu Hu253776.69
Shun Lu301.01
Longxing Yang400.68
Jilin Mei501.01
Yinhe Han666667.18
Xinrong Li71266157.76