Transformer Model Interpretation Using Attention and Gradient Links
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Solution Overview
Problem
Existing artificial intelligence models, particularly deep learning models like transformers, lack interpretability, leading to poor universality and hardware performance due to users' inability to understand how they make decisions.
Innovation Solution
A method that determines an association relation between token vectors and model prediction results using attention weights and gradients to enhance interpretability, allowing users to understand the internal workings of the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a deep learning model is used for image processing, then processing speed and accuracy are improved, but model interpretability deteriorates
Solution Approach 1:
The patent introduces an intermediary module that computes attention weights and gradients to bridge the gap between the black-box model and human understanding. This intermediary provides explanatory information about model decision-making without altering the core processing functionality, thus maintaining processing speed while improving interpretability.
Solution Approach 2:
The patent segments the model interpretation process into distinct components: attention weight calculation, gradient computation, and association relation determination. This segmentation allows each component to be optimized independently and facilitates better understanding of how the model processes information while maintaining overall processing efficiency.
2Measurement precision
If a deep learning model is used for image processing, then processing accuracy is improved, but model interpretability deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where attention weights and gradients are computed and used to generate explanations that feed back into the interpretation process. This feedback loop enables continuous refinement of model understanding while preserving the high accuracy of the underlying deep learning model for image processing tasks.
3Adaptability or versatility
If model interpretability is enhanced through additional processing, then universality and reliability are improved, but device complexity increases
Solution Approach 1:
The patent designs the attention weight and gradient computation mechanism to be universally applicable across different deep learning model architectures and image processing tasks. By creating a multi-functional interpretation framework that works with various models, the patent enhances universality without requiring separate complex systems for each model type.
Data Source
AI summary
Provided is a model interpretation method, an image processing method, an electronic device and a storage medium, relating to the field of artificial intelligence, in particular to the field of deep learning. The model interpretation method includes: obtaining a token vector corresponding to an image feature input to a first model; obtaining a model prediction result output by the first model; and determining, according to a combination of an attention weight and a gradient, an association relation between the token vector input to the first model and the model prediction result output by the first model, where the association relation is used to characterize interpretability of the first model.