Neural Network Weight Initialization via Mean-Variance Mapping
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Solution Overview
Problem
Current weight initialization techniques for deep learning neural networks are inadequate as they fail to universally preserve mean and variance across layers, leading to issues like exploding or vanishing output signals, and are often specific to certain activation functions, topologies, or require manual derivation, which is time-consuming and inefficient.
Innovation Solution
A method and system that derive a mean-variance mapping function for each layer to analytically preserve the mean and variance of output signals, allowing for dynamic adaptation and appropriate weight initialization across various neural network architectures, including new layer types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional weight initialization techniques are used, then the neural network can be initialized, but the output signals may explode or vanish across layers
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting weight initialization parameters (mean and variance) based on layer-specific characteristics. The system derives mean-variance mapping functions that transform input signal statistics to desired output signal statistics, changing the initialization parameters to prevent signal explosion or vanishing across different layer types and depths.
Solution Approach 2:
The patent implements preliminary action by pre-computing mean-variance mapping functions for each layer before training begins. These functions are derived analytically based on the layer's activation function, topology, and desired signal statistics, allowing the network to start training with optimally initialized weights that prevent signal pathologies from the outset.
2Measurement precision
If manual derivation of weight initialization techniques is performed for each architecture, then accuracy can be optimized, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent implements self-service by enabling the system to automatically derive mean-variance mapping functions for any given layer configuration without human intervention. The automated derivation process takes layer specifications (activation function, topology) as input and generates optimized initialization parameters, eliminating the need for manual mathematical derivation while maintaining high accuracy.
Solution Approach 2:
The patent applies universality by creating a general framework that can handle multiple layer types (dense, convolutional, recurrent, normalization) and various activation functions through a unified mean-variance mapping approach. This universal method eliminates the need for architecture-specific manual derivation while maintaining optimization accuracy across diverse neural network designs.
3Ease of manufacture
If existing weight initialization techniques are used, then implementation is simple, but they are specific to certain activation functions and topologies
Solution Approach 1:
The patent applies universality by designing a framework that works across all layer types and activation functions through mean-variance mapping. The system automatically adapts the initialization strategy to the specific layer configuration while maintaining a unified implementation approach, achieving both simplicity and versatility simultaneously.
Solution Approach 2:
The patent implements dynamics by making the weight initialization adaptive to the specific layer characteristics rather than using fixed initialization rules. The mean-variance mapping functions are derived dynamically based on the layer's activation function, topology, and desired signal statistics, allowing the system to adapt to any architecture while maintaining implementation simplicity through automation.
Data Source
AI summary
The present invention provides a system and a method for evaluating weight initialization techniques for individual layers of neural network model by preserving mean and variance of output signals propagated through respective layers of model. In operation, the present invention provides for deriving a mean-variance mapping function (g-layer) for each layer of received neural network model. Further, the present invention provides for determining if weight parameter is associated with respective layers of model. Furthermore, weight initialization technique is evaluated for setting initial value of weight parameter of layers determined to have weight parameter by using derived mean-variance mapping functions, such that mean of output signal of respective layers is zero and variance is one. The preserving of mean and variance of output signal across respective layers to zero and one respectively ensures that the weight parameter is initialized properly, further eliminating the problem of exploding and/or vanishing output signals.