Deep Learning Model Interpretability via Dual-Model Weight Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deep learning models, despite their accuracy, lack interpretability, making it challenging to determine the reasons behind their predictions, which is crucial for applications like fraud detection where real-time explanations are needed.
Innovation Solution
A method involving training a first model and a second model using training data, extracting weights from the second model, and applying them to input data to calculate feature contributions, enabling real-time determination of feature contributions to predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models with millions of parameters are used, then accuracy and generalization performance are improved, but interpretability deteriorates
Solution Approach 1:
The patent segments the complex deep learning model into two separate models: a first deep learning model for accurate prediction and a second simplified model for interpretability. The second model is trained using the predictions from the first model as training data, allowing the system to extract meaningful feature contributions without analyzing the complex internal structure of the original deep learning model directly.
Solution Approach 2:
The patent introduces a second simplified model as an intermediary between the complex first model and the user seeking interpretation. This intermediary model translates the complex predictions into understandable feature contributions, acting as a mediator that bridges the gap between accurate but opaque deep learning predictions and human-comprehensible explanations.
2Reliability
If complex deep learning models are deployed for real-time decision making, then decision quality is improved, but the ability to provide real-time explanations deteriorates
Solution Approach 1:
The patent performs preliminary action by training the second simplified model in advance using predictions from the first model. This pre-training process captures the essential decision-making patterns of the complex model without requiring real-time analysis of its complex structure. During real-time operation, only the lightweight second model needs to be executed for interpretation, significantly reducing explanation generation time.
Solution Approach 2:
The patent creates a simplified copy (the second model) that replicates the essential predictive behavior of the complex first model. This copy is trained on the predictions of the original model, allowing it to mimic the decision-making process in a much simpler and faster manner, enabling real-time explanations without sacrificing decision quality.
Data Source
AI summary
A system, method, and product for determining a reason for a deep learning model output that obtain training data associated with training samples and first labels for the training samples; train a first model using the training samples and the first labels, training the first model generating predictions for the training samples; train a second model using the training samples and the predictions as second labels for the training samples; extract one or more weights of the trained second model; process, using the first model, input data including features associated with at least one sample, to generate output data, the output data including at least one prediction for the at least one sample; and apply the one or more extracted weights to the features to determine one or more contributions of one or more features of the features to the at least one prediction for the at least one sample.