Neural Network Training via Second Network Gradient Approximation
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Solution Overview
Problem
The existing neural network training methods require complex calculations for second-order differentials to adjust gradients, which can be computationally intensive due to the need for Hessian matrix calculations.
Innovation Solution
A method involving a first neural network to estimate output data and a second neural network to generate output differential values with respect to input data, where the second neural network differentiates the output of each layer of the first neural network using shared parameters, allowing for training without direct calculation of second-order differentials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If second-order differential calculations are performed to calculate gradients for neural network training, then training accuracy can be improved, but computational complexity and calculation time increase significantly
Solution Approach 1:
The patent introduces a second neural network as an intermediary component that approximates the second-order differential calculations. Instead of directly computing complex second-order derivatives, the system uses the second neural network to generate differential values based on input data and first-order differential data from the first neural network, significantly reducing computational complexity while maintaining training accuracy
Solution Approach 2:
The patent creates a copy of the neural network architecture (the second neural network) that replicates the computational function of calculating second-order differentials. This copied network takes first-order differential data as input and produces the required differential values, avoiding the need for direct second-order differentiation while preserving the necessary computational functionality
2Measurement precision
If Hessian matrix calculations are performed to obtain second-order differentials, then gradient accuracy improves, but processing time and energy consumption increase
Solution Approach 1:
The patent replaces the mechanical computation of Hessian matrices with a neural network-based system. The second neural network substitutes for the complex mathematical operations of second-order differentiation, enabling gradient accuracy to be maintained while processing time is significantly reduced through learned patterns rather than brute-force calculation
3Reliability
If second-order differential calculations are performed for all input data, then training completeness is ensured, but computational efficiency decreases
Solution Approach 1:
The patent applies partial action by determining whether second-order differential calculations are necessary for each input data sample. The system uses the second neural network to generate differential values selectively, avoiding unnecessary computations for inputs where first-order differentials suffice, thus maintaining training completeness while improving computational efficiency
Data Source
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AI summary
A processor-implemented method may include generating respective first neural network differential data by differentiating a respective output of each layer of a first neural network with respect to input data provided to the first neural network that estimates output data from the input data, by a forward propagation process of the first neural network, generating, using a second neural network, an output differential value of the output data with respect to the input data using the respective first neural network differential data, and training the first neural network and the second neural network based on ground truth data of the output data and ground truth data of the output differential value.