Layer-wise Attribution Analysis for Computer Vision Explainability
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Solution Overview
Problem
Advanced computer vision systems using deep learning models analyze images at multiple layers, but provide limited explainability, making it difficult to identify errors, ensure compliance, and improve model accuracy.
Innovation Solution
An image analysis platform performs layer-wise attribution image analysis, fusing attributions from multiple layers to provide an assessment of predictions, enhancing explainability and enabling improved model training and deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models analyze images at multiple layers, then the analysis comprehensiveness is improved, but the explainability deteriorates
Solution Approach 1:
The patent segments the deep learning model into multiple layers and generates separate attribution maps for each layer. This segmentation allows the system to analyze what each individual layer contributes to the final prediction, making the black-box model interpretable by breaking it down into understandable components that can be visualized and assessed independently
Solution Approach 2:
The patent introduces an intermediary assessment mechanism that compares attribution maps across different layers. This intermediary process evaluates whether layers are contributing meaningfully and consistently to the prediction, acting as a mediator between the complex internal model operations and the need for explainable outputs
2Loss of information
If layer-wise attribution analysis is performed, then the explainability is improved, but the computational complexity increases
Solution Approach 1:
The system performs self-assessment by automatically generating and comparing attribution maps across layers without requiring external manual analysis. The model serves its own explainability needs through internal mechanisms that evaluate its own layer contributions, reducing the need for complex external interpretation tools
3Measurement precision
If structural similarities between layers are assessed, then the model accuracy is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary assessment of layer attributions during the model execution process itself, rather than requiring separate post-processing analysis. By evaluating layer contributions as part of the forward pass, the system prepares explainability data in advance, enabling faster deployment and reduced processing time for accuracy assessment
Data Source
AI summary
In some implementations, an image analysis platform may receive image data for analysis. The image analysis platform may generate, using a computer vision model, an analysis of the image data, wherein the analysis comprises a plurality of layers generated from the image data. The image analysis platform may determine attributions of each layer to a prediction based on the analysis of the image data. The image analysis platform may determine structural similarities between image pairs associated with sets of layers of the plurality of layers, wherein the structural similarities are based on the attributions. The image analysis platform may analyze the structural similarities to determine an assessment of the prediction performed using the computer vision model. The image analysis platform may provide an output associated with identifying the assessment of the prediction.