Interpretable Convolutional Sequence Model with Resolution-Controllable Prototypes
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Solution Overview
Problem
Deep learning models for sequence data, such as LSTM and CNNs, lack interpretability, making it difficult for end-users to trust their predictions, especially in critical domains like healthcare and finance, where transparency is essential.
Innovation Solution
The proposed method, SCNpro, combines a convolutional sequence model with prototype learning to provide interpretable predictions by converting input sequences into output features, clustering them using resolution-controllable class prototypes, calculating similarity scores, and performing predictions with non-negative weights in a fully connected layer, offering transparent reasoning for model decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models (LSTM, CNNs) are used for sequence data processing, then prediction accuracy is improved, but interpretability deteriorates
Solution Approach 1:
The patent introduces prototypes as intermediary elements that bridge the gap between the black-box deep learning model and human interpreters. These prototypes serve as mediating representations that capture essential patterns from training data while remaining understandable to users, thus preserving interpretability while maintaining the predictive power of complex models
2Reliability
If complex deep learning models are deployed in decision critical domains, then prediction capability is improved, but user trust deteriorates
Solution Approach 1:
The model performs self-explanation by automatically generating prototypes and similarity scores that reveal its reasoning process. This self-service mechanism allows the model to provide its own interpretability without external assistance, thereby maintaining both high prediction capability and user trust in decision-critical domains
Data Source
AI summary
A method interprets a convolutional sequence model. The method converts an input data sequence having input segments into output features. The method clusters the input segments into clusters using respective resolution-controllable class prototypes allocated to each of classes. Each respective class prototype includes a respective output feature subset characterizing a respective associated class. The method calculates, using the clusters, similarity scores that indicate a similarity of an output feature to a respective class prototypes responsive to distances between the output feature and the respective class prototypes. The method concatenates the similarity scores to obtain a similarity vector. The method performs a prediction and prediction support operation that provides a value of prediction and an interpretation for the value responsive to the input segments and similarity vector. The interpretation for the value of prediction is provided using only non-negative weights and lacking a weight bias in the fully connected layer.