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OStr-DARTS: Differentiable Neural Architecture Search Based on Operation Strength
IEEE Transactions on CyberneticsDifferentiable architecture search (DARTS) has emerged as a promising technique for effective neural architecture search, and it mainly contains two steps to find the high-performance architecture: First, the DARTS supernet that consists of mixed operations will be optimized via gradient descent.
Yizeng Han, Gao Huang, Ziwei Zheng
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An Architecture Entropy Regularizer for Differentiable Neural Architecture Search
Neural Networks, 2023Differentiable architecture search (DARTS) is one of the prevailing paradigms of neural architecture search (NAS) due to allowing efficient gradient-based optimization during the search phase. However, its poor stability and generalizability are intolerable. We argue that the crux is the locally optimal architecture parameter caused by a dilemma, which
Kun Jing, Jungang Xu
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Att-DARTS: Differentiable Neural Architecture Search for Attention
2020 International Joint Conference on Neural Networks (IJCNN), 2020Neural architecture search (NAS) is a promising method to automatically identify neural network architectures. Differentiable architecture search (DARTS) is method that significantly reduces search time and finds architectures that can achieve state-of-the-art performance.
Kohei Nakai +2 more
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Decoupled Differentiable Graph Neural Architecture Search
Information SciencesJianliang Gao +2 more
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Differentiable Neural Architecture Search for Domain Adaptation in Fault Diagnosis
Mechanical Systems and Signal Processing, 2023Yang Hu, Xudong Li, Yumeng Liu
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FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Differentiable Neural Architecture Search (DNAS) has demonstrated great success in designing state-of-the-art, efficient neural networks. However, DARTS-based DNAS's search space is small when compared to other search methods', since all candidate network layers must be explicitly instantiated in memory.
Saining Xie, Zijian He
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Differentiable Architecture Search With Multi-Dimensional Attention for Spiking Neural Networks
NeurocomputingSpiking Neural Networks (SNNs) have gained enormous popularity in the field of artificial intelligence due to their low power consumption. However, the majority of SNN methods directly inherit the structure of Artificial Neural Networks (ANN), usually ...
Yumei Zhou, Delong Shang, Yilei Man
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Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search
ACM Computing SurveysIn the past few years, Differentiable Neural Architecture Search (DNAS) rapidly imposed itself as the trending approach to automate the discovery of deep neural network architectures. This rise is mainly due to the popularity of DARTS (Differentiable ARchitecTure Search), one of the first major DNAS methods.
Hichem Arioui +2 more
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DMNAS: Differentiable Multi-modal Neural Architecture Search
International Workshop on Advanced Imaging Technology (IWAIT) 2021, 2021This paper proposes a Neural Architecture Search (NAS) method for multimodal sequential data using a gradient-based neural architecture search method named Differentiable Neural Architecture Search (DARTS). Because Deep Neural Networks (DNNs) for multimodal data require task-specific network architecture, there is a high need for NAS for them to reduce
Yushiro Funoki, Satoshi Ono
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Dependency-Aware Differentiable Neural Architecture Search
Neural architecture search (NAS) reduces the burden of manual design by automatically building neural network architectures, among which differential NAS approaches such as DARTS, have gained popularity for the search efficiency. Despite achieving promising performance, the DARTS series methods still suffer two issues: 1) It does not explicitly ...Buang Zhang +4 more
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