Corpus ID: 232105001

Differentiable Neural Architecture Learning for Efficient Neural Network Design

@article{Guo2021DifferentiableNA,
  title={Differentiable Neural Architecture Learning for Efficient Neural Network Design},
  author={Qingbei Guo and Xiao-Jun Wu and J. Kittler and Zhiquan Feng},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.02126}
}
Automated neural network design has received everincreasing attention with the evolution of deep convolutional neural networks (CNNs), especially involving their deployment on embedded and mobile platforms. One of the biggest problems that neural architecture search (NAS) confronts is that a large number of candidate neural architectures are required to train, using, for instance, reinforcement learning and evolutionary optimisation algorithms, at a vast computation cost. Even recent… Expand

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