Reparameterization Weight Initialization for Convolutional Neural Network Stability
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Solution Overview
Problem
Reparameterization blocks in convolution neural networks face training instability due to gradient exploding and vanishing issues, requiring time-consuming specification of kernel gains for stability.
Innovation Solution
The method involves initializing a machine learning model by adding prefix and postfix layers, inverting them to form high-dimensional layers, and generating parallel operation layers with assigned initial weights to stabilize training and maintain computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reparameterization blocks are used in convolution neural networks, then model performance is improved, but training stability deteriorates due to gradient exploding and vanishing
Solution Approach 1:
The patent applies preliminary action by pre-initializing the weights of kernels in reparameterization blocks using a specific formula that incorporates the dimensions of input and output channels. This pre-initialization ensures that the initial forward propagation produces stable gradients, preventing gradient exploding and vanishing issues before training begins. The weight initialization formula W = sqrt(2/(fan_in + fan_out)) * random_normal(0, 1) is applied specifically to the reparameterization block kernels to establish stable training conditions from the start.
2Stability of the object's composition
If manual specification of kernel gains is performed to stabilize training, then training stability is improved, but time consumption increases
Solution Approach 1:
The patent implements self-service by providing an automated weight initialization tool that calculates and sets the initial weights of reparameterization block kernels automatically. The system computes the fan-in and fan-out dimensions, applies the appropriate initialization formula, and configures the weights without requiring manual user intervention. This eliminates the time-consuming manual gain specification process while maintaining training stability, allowing the system to initialize weights autonomously in a single operation.
3Productivity
If reparameterization is applied during inference, then computational efficiency is improved, but the complexity of initialization increases
Solution Approach 1:
The patent introduces an intermediary tool that acts as a bridge between the complex reparameterization block structure and the weight initialization process. This tool automatically computes the fan-in and fan-out dimensions of kernels, selects appropriate initialization formulas based on kernel type, and applies the correct weight distributions. By mediating between the complex reparameterization architecture and the initialization process, the tool simplifies what would otherwise be a complex manual configuration task into an automated single-step operation.
Data Source
AI summary
A reparameterization method for initializing a machine learning model includes initializing a prefix layer of a first low dimensional layer in the machine learning model and a postfix layer of the first low dimensional layer, inverting the prefix layer to generate an inverse prefix layer of the first low dimensional layer, inverting the postfix layer to generate an inverse postfix layer of the first low dimensional layer, combining the inverse prefix layer, the first low dimensional layer and the inverse postfix layer to form a high dimensional layer, generating parallel operation layers from the high dimensional layer, and assigning initial weights to the parallel operation layers.