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Discovering Parametric Activation Functions.

Garrett Bingham1, Risto Miikkulainen1

  • 1The University of Texas at Austin, Austin, TX, 78712, USA; Cognizant AI Labs, 649 Front St., San Francisco, CA, 94111, USA.

Neural Networks : the Official Journal of the International Neural Network Society
|January 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method to customize deep learning activation functions, significantly boosting performance beyond standard Rectified Linear Units (ReLU) for image classification tasks.

Keywords:
Activation functionsAutoMLDeep learningEvolutionary computationGradient descent

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Area of Science:

  • Deep Learning
  • Artificial Intelligence
  • Machine Learning

Background:

  • Activation functions are crucial for deep learning network performance.
  • The Rectified Linear Unit (ReLU) is widely used due to inconsistent benefits of novel functions.
  • Task-dependent performance of activation functions necessitates optimization.

Purpose of the Study:

  • To propose a technique for automatically customizing activation functions.
  • To achieve reliable performance improvements in deep learning models.
  • To address the limitations of standard activation functions like ReLU.

Main Methods:

  • Utilizing evolutionary search to discover general activation function forms.
  • Employing gradient descent to optimize function parameters.
  • Applying the technique across different neural network architectures and datasets (CIFAR-10, CIFAR-100).

Main Results:

  • The proposed approach effectively discovers both general and specialized activation functions.
  • Consistent accuracy improvements were observed over ReLU and other activation functions.
  • The method demonstrated effectiveness across various neural network architectures.

Conclusions:

  • Automated activation function customization offers reliable performance gains.
  • This technique can serve as an automated optimization step for new deep learning tasks.
  • The approach enhances deep learning model accuracy for image classification.