Fractional Adaptive Linear Unit Activation Function for Neural Networks
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Solution Overview
Problem
Existing activation functions in Artificial Neural Networks (ANNs), such as Sigmoid, ReLU, and Swish, suffer from limitations like the vanishing gradient phenomenon, limiting the performance of deeper architectures and failing to provide flexibility and expressiveness.
Innovation Solution
The introduction of a trainable activation function called FALU (Fractional Adaptive Linear Unit) with tunable parameters, including a fractional derivative order and scaling parameter, which can generate a family of activation functions, including Sigmoid, ReLU, and Swish, allowing for dynamic generalization and enhanced performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional activation functions (Sigmoid, ReLU, Swish) are used, then the network structure is simple and easy to implement, but the vanishing gradient phenomenon occurs and performance of deeper architectures is limited
Solution Approach 1:
The patent introduces a dynamic activation function FALU(x; α, β) with two learnable parameters α and β that can be automatically adjusted during training. This dynamic adaptation allows the activation function to overcome the vanishing gradient problem in deeper architectures by automatically optimizing its behavior, resolving the contradiction between network depth and gradient flow.
Solution Approach 2:
The patent changes the parameters of the activation function from fixed (traditional Sigmoid, ReLU) to variable (FALU with learnable α and β). By introducing parameter adaptability, the system can adjust the activation characteristics during training to prevent vanishing gradients while maintaining computational efficiency, thus improving deep architecture performance without excessive complexity.
2Adaptability or versatility
If fixed activation functions are used, then the implementation is simple, but flexibility and expressiveness are insufficient
Solution Approach 1:
The FALU activation function introduces dynamic parameter learning capability, allowing the system to adapt to different data distributions and network depths automatically. This dynamic flexibility enables the same activation function to perform optimally across various tasks without requiring manual redesign, achieving high adaptability while keeping the core implementation relatively simple.
Solution Approach 2:
The patent creates a universal activation function FALU that can serve multiple purposes: it generalizes traditional activation functions, adapts to different network depths, and works across various tasks. This multi-functionality provides high flexibility and expressiveness while avoiding the need for multiple specialized activation functions, thus managing complexity effectively.
3Reliability
If traditional activation functions are used, then the computational cost is low, but the capability and performance of ANNs are limited
Solution Approach 1:
The patent modifies the activation function by introducing two learnable parameters α and β that are updated during training. This parameter adaptation enables the function to capture more complex patterns and improve network capability. The additional computational cost is justified by the significant performance improvements in tasks like image classification and natural language processing.
Solution Approach 2:
The FALU activation function incorporates feedback through automatic parameter adjustment during training based on gradient information. This feedback mechanism allows the system to optimize its own performance by adapting parameters according to training outcomes, achieving enhanced capability while managing computational resources efficiently through data-driven optimization.
Data Source
AI summary
An apparatus is provided for deep learning. The apparatus accesses a neural network including an input layer, hidden layers, and an output layer. The apparatus adds an activation function to one or more of the hidden layers of the hidden layers and output layer. The activation function includes a tunable parameter, the value of which can be adjusted during the training of the neural network. The apparatus trains the neural network by inputting training samples into the neural network and determining internal parameters of the neural network based on the training samples. Determining the internal parameters includes determining a value of the tunable parameter based on the training samples. The apparatus may determine two different values of the tunable parameter for two different layers. The activation function may include another tunable parameter. The apparatus can determine a value for the other tunable parameter during the training of the neural network.