Online parts-based feature discovery using competitive activation neural networks

In the first wave of artificial intelligence (AI), rule-based expert systems were developed, with modest success, to help generalists who lacked expertise in a specific domain. The second wave of AI, originally called artificial neural networks but now described as machine learning, began to have an impact with multilayer networks in the 1980s. Deep learning, which enables automated feature discovery, has enjoyed spectacular success in several medical disciplines, including cardiology, from automated image analysis to the identification of the electrocardiographic signature of atrial fibrillation during sinus rhythm. Machine learning is now embedded within the NHS Long-Term Plan in England, but its widespread adoption may be limited by the “black-box” nature of deep neural networks.

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Feature discovery on segmented objects in SAR imagery using self-organizing neural networks

10.1117/12.152579 ◽

1993 ◽

Cited By ~ 3

Author(s):

Robert J. Fogler ◽

Mark W. Koch ◽

Mary M. Moya ◽

Donald R. Hush

Keyword(s):

Neural Networks ◽

Sar Imagery ◽

Feature Discovery ◽

Self Organizing

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Improving Convolutional Neural Networks with Competitive Activation Function

Security and Communication Networks ◽

10.1155/2021/1933490 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Yao Ying ◽

Nengbo Zhang ◽

Ping He ◽

Silong Peng

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Activation Function ◽

Linear Mapping ◽

Nonlinear Transformation ◽

Nonlinear Mapping ◽

Activation Functions ◽

Competition Mechanism ◽

Function Design ◽

Competitive Activation

The activation function is the basic component of the convolutional neural network (CNN), which provides the nonlinear transformation capability required by the network. Many activation functions make the original input compete with different linear or nonlinear mapping terms to obtain different nonlinear transformation capabilities. Until recently, the original input of funnel activation (FReLU) competed with the spatial conditions, so FReLU not only has the ability of nonlinear transformation but also has the ability of pixelwise modeling. We summarize the competition mechanism in the activation function and then propose a novel activation function design template: competitive activation function (CAF), which promotes competition among different elements. CAF generalizes all activation functions that use competition mechanisms. According to CAF, we propose a parametric funnel rectified exponential unit (PFREU). PFREU promotes competition among linear mapping, nonlinear mapping, and spatial conditions. We conduct experiments on four datasets of different sizes, and the experimental results of three classical convolutional neural networks proved the superiority of our method.

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