Explainable Deep Interpolation via Neural Weight Maps
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Solution Overview
Problem
Existing interpolation techniques are either transparent but weak for low-dimensionality problems or strong but opaque for high-dimensionality problems, lacking explainability and reliability in complex scenarios.
Innovation Solution
A system that uses a deep learning neural network to generate weight maps for missing numerical elements, allowing for explainable interpolation by combining these maps with available neighbors, providing transparency into the interpolation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning techniques are used for interpolation, then interpolation performance is improved, but interpretability is worsened
Solution Approach 1:
The patent introduces weight maps as an intermediary between the deep learning model and the interpolation output. These weight maps provide interpretability by showing which input pixels contribute to each output pixel, while the underlying deep learning model maintains high interpolation performance. The weight maps act as a mediator that translates the black-box deep learning operations into human-understandable contributions.
Solution Approach 2:
The patent segments the interpolation process into distinct components: the deep learning model for performance and the weight map generation for interpretability. By separating these functions, the system can optimize each component independently - the deep learning model for accuracy and the weight maps for explainability - while maintaining both properties in the overall system.
2Loss of information
If analytical interpolation techniques are used, then interpretability is improved, but reliability for high-dimensionality problems is worsened
Solution Approach 1:
The patent merges the strengths of both analytical and deep learning approaches. It combines the interpretability of analytical methods (through weight maps that show clear contribution patterns) with the power of deep learning for handling high-dimensionality problems. The system integrates these two paradigms into a unified framework that achieves both interpretability and reliability.
3Loss of information
If deep learning neural networks are used to generate weight maps, then transparency is improved, but computational complexity is worsened
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing weight maps during the forward pass of the neural network. These pre-computed weight maps are then reused for interpretation purposes without requiring additional complex computations. This preliminary computation of transparency information reduces the overall computational burden during the interpretation phase.
Data Source
AI summary
Systems/techniques that facilitate explainable deep interpolation are provided. In various embodiments, a system can access a data candidate, wherein a set of numerical elements of the data candidate are missing. In various aspects, the system can generate, via execution of a deep learning neural network on the data candidate, a set of weight maps for the set of missing numerical elements. In various instances, the system can compute the set of missing numerical elements by respectively combining, according to the set of weight maps, available interpolation neighbors of the set of missing numerical elements.