FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks

@article{Qiu2017FReLUFR,
  title={FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks},
  author={Suo Qiu and Bolun Cai},
  journal={2018 24th International Conference on Pattern Recognition (ICPR)},
  year={2017},
  pages={1223-1228}
}
  • Suo QiuBolun Cai
  • Published 25 June 2017
  • Computer Science
  • 2018 24th International Conference on Pattern Recognition (ICPR)
Rectified linear unit (ReLU) is a widely used activation function for deep convolutional neural networks. However, because of the zero-hard rectification, ReLU networks lose the benefits from negative values. In this paper, we propose a novel activation function called flexible rectified linear unit (FReLU) to further explore the effects of negative values. By redesigning the rectified point of ReLU as a learnable parameter, FReLU expands the states of the activation output. When a network is… 

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