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Clevert, D.-A., Unterthiner, T. and Hochreiter, S. (2016) Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). Paper Presented at the Meeting of the 4th International Conference on Learning Representations (Poster), San Juan, 2-4 May 2016.
http://arxiv.org/abs/1511.07289

has been cited by the following article:

• TITLE: Activation Function: Cell Recognition Based on YoLov5s/m

  AUTHORS: Zuomin Yang

  KEYWORDS: Mish_PLUS, Sigmoid_Tanh, Cell Recognition

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.12, December 22, 2021

  ABSTRACT: Activation functions play a critical role in neural networks. The paper mainly studies activation functions with four activation functions that were the selection for reference and comparison. The Mish activation function was expending as the Mish_PLUS activation function, the Sigmoid activation function, and the Tanh were combined to obtain a new Sigmoid_Tanh activation function. We used the recently popular YoLov5s and YoLov5m as the basic structure of the neural network. The function realized in this article was the recognition function of red blood cells, white blood cells, and platelets. Through the role and comparison of different activation functions in the neural network structure, the test results show that, in this paper, the training precision curve under the Sigmoid_Tanh activation function was better than that under the action of other activation functions. That means that the accuracy of cell recognition under the activation function was higher.