Semantic Variance Quantification for Neural Network Representations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods fail to accurately measure semantic information variance between two neural network representations, limiting the ability to analyze and compare the internal mechanisms of neural networks.
Innovation Solution
A method and system that quantify semantic variance by extracting representations from two neural networks, learning weights for each filter using the Net2Vec method, calculating set IoU for each semantic concept, and integrating variance between the set IoU of the two representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If visualization methods (activation maximization) are used to interpret neural networks, then semantic concepts can be decoded from intermediate layers, but the method cannot measure semantic information variance between two neural network representations
Solution Approach 1:
The patent transforms the interpretation problem from qualitative visualization to quantitative measurement by changing the parameter representation. Instead of using activation maximization to visualize semantic concepts, the patent learns weights for each filter corresponding to semantic concepts and calculates IoU metrics. This parameter transformation enables the measurement of semantic information variance between different neural network representations, directly resolving the inability to quantify semantic differences.
Solution Approach 2:
The patent replaces the visualization-based interpretation mechanism with a measurement-based mechanism. Instead of using activation maximization to decode semantic concepts visually, the patent employs a systematic approach involving weight learning, IoU calculation, and variance integration. This substitution transforms the interpretation process from a qualitative visual analysis to a quantitative measurement system, enabling precise comparison of semantic information between representations.
2Loss of information
If statistical distribution methods are used to analyze intermediate layer filters, then distribution forms (number of filters, IoU values) can be collected, but the method cannot measure semantic information variance between two neural network representations
Solution Approach 1:
The patent segments the semantic variance measurement process into distinct components: (1) learning weights for each filter corresponding to semantic concepts, (2) calculating IoU for each representation across all semantic concepts, and (3) integrating variance across all semantic concepts. This segmentation allows the system to measure semantic information variance by breaking down the complex task into manageable steps, each handling a specific aspect of the measurement process.
Solution Approach 2:
The patent extends the analysis from single-representation statistics to multi-representation comparison by adding a new dimension. Instead of only analyzing the distribution of filters within one representation, the patent calculates IoU for multiple representations and integrates the variance across them. This dimensional extension enables the measurement of semantic information variance between different neural network representations.
Data Source
AI summary
A method and system for quantifying semantic variance between neural network representations is provided. Two neural network representations to be compared are first extracted, the weight of each filter in an intermediate layer corresponding to each semantic concept is learned on a reference dataset using the Net2Vec method, then set IoU of each representation for all semantic concepts in the reference dataset are calculated, and finally variance between the set IoU of the two representations for all the semantic concepts are integrated to obtain semantic variance between the two neural network representations. The method solves the problem of lack of accurate measurement on the variance between neural network representations on a semantic information level, and has an accurate measurement effect.