Neural-Architecture-Search-Based Multiobjective Cognitive Automation System

Wang, Eric Ke; Xu, Ship Peng; Chen, Chien‐Ming; Kumar, Neeraj

doi:10.1109/jsyst.2020.3002428

Cited by 7 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Introductionmentioning

confidence: 99%

“…In recent advances in deep learning research, neural architecture search (NAS) is one of emerging fields, which aims to design novel NN architectures with automated pipelines [12][13][14][15][16][17]. The concept of search space with an RL-based controller has been introduced by Zoph et al [18], and NASNet [19] has achieved better accuracy of discovered architecture than manually designed architectures.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Two‐stage architectural fine‐tuning for neural architecture search in efficient transfer learning

Park,

Son,

Lee

et al. 2023

Electronics Letters

View full text Add to dashboard Cite

In many deep neural network (DNN) applications, the difficulty of gathering high‐quality data in industry fields hinders the practical use of DNN. Thus, the concept of transfer learning (TL) has emerged, which leverages the pretrained knowledge of the DNN which was built based on large‐scale datasets. For this TL objective, this paper suggests two‐stage architectural fine‐tuning for reducing the costs and time while exploring the most efficient DNN model, inspired by neural architecture search (NAS). The first stage is mutation, which reduces the search costs using a priori architectural information. Moreover, the next stage is early‐stopping, which reduces NAS costs by terminating the search process in the middle of computation. The data‐intensive experimental results verify that the proposed method outperforms benchmarks.

show abstract

Section: Introductionmentioning

confidence: 99%