Neural architecture search

Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment
The ongoing advancements in network architecture design have led to remarkable achievements in deep learning across various challenging …
Zhichao Lu, Ran Cheng, Yaochu Jin, Kay Chen Tan, Kalyanmoy Deb
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Surrogate-assisted Multiobjective Neural Architecture Search for Real-time Semantic Segmentation
The architectural advancements in deep neural networks have led to remarkable leap-forwards across a broad array of computer vision …
Zhichao Lu, Ran Cheng, Shihua Huang, Haoming Zhang, Changxiao Qiu, Fan Yang
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Accelerating multi-objective neural architecture search by random-weight evaluation
For the goal of automated design of high-performance deep convolutional neural networks (CNNs), neural architecture search (NAS) …
Shengran Hu, Ran Cheng, Cheng He, Zhichao Lu, Jing Wang, Miao Zhang
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Efficient Evolutionary Neural Architecture Search by Modular Inheritable Crossover
Deep neural networks are widely used in the domain of image classification, and a large number of excellent deep neural networks have …
Cheng He, Hao Tan, Shihua Huang, Ran Cheng
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Multi-objective Neural Architecture Search with Almost No Training
In the recent past, neural architecture search (NAS) has attracted increasing attention from both academia and industries. Despite the …
Shengran Hu, Ran Cheng, Cheng He, Zhichao Lu
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Efficient Evolutionary Deep Neural Architecture Search (NAS) by Noisy Network Morphism Mutation
Deep learning has achieved enormous breakthroughs in the field of image recognition. However, due to the time-consuming and error-prone …
Yiming Chen, Tianci Pan, Cheng He, Ran Cheng
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