Deep Neural Network Feature Visualization via Synthesis
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Solution Overview
Problem
Deep neural networks operate as 'black boxes,' making it difficult to explain or visualize how they make decisions, particularly in identifying features relevant to outcomes, which hinders understanding and validation of their operations.
Innovation Solution
A method and apparatus that provide a representation of features identified by deep neural networks by creating a feature recognition library, synthesizing elements that trigger recognition, and using generative modeling to visualize these features, enabling better understanding and improvement of the network's operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If deep neural networks are used to identify features from data, then automation and productivity are improved, but the system becomes a black box that is difficult to explain or visualize
Solution Approach 1:
The patent introduces an intermediary visualization system that translates the internal feature representations of the deep neural network into human-interpretable visual forms. This mediator layer includes components that extract feature activations, generate visual representations, and display them in ways that reveal the network's decision-making process without altering the automated feature identification capability.
2Measurement precision
If deep neural networks process large datasets to learn features, then measurement precision and reliability are improved, but the complexity of understanding and validating the network increases
Solution Approach 1:
The patent extracts specific feature representations and activations from the deep neural network's internal processing layers. By isolating and examining individual feature activations separately from the overall complex network operation, the system enables precise analysis of what the network has learned without requiring understanding of the entire complex system at once.
3Ease of operation
If the deep neural network operates as a black box, then ease of operation is improved, but the ability to understand and improve model performance is reduced
Solution Approach 1:
The patent implements a feedback mechanism where visualized feature representations are fed back to developers and domain experts. This feedback loop enables them to understand what features the network has learned, validate whether these features are meaningful, and use this insight to improve the network's performance through better data collection, feature engineering, or architecture modifications.
Data Source
AI summary
Aspects and embodiments relate to a method of providing a representation of a feature identified by a deep neural network as being relevant to an outcome, a computer program product and apparatus configured to perform that method. The method comprises: providing the deep neural network with a training library comprising: a plurality of samples associated with the outcome; using the deep neural network to recognise a feature in the plurality of samples associated with the outcome; creating a feature recognition library from an input library by identifying one or more elements in each of a plurality of samples in the input library which trigger recognition of the feature by the deep neural network; using the feature recognition library to synthesise a plurality of one or more elements of a sample which have characteristics which trigger recognition of the feature by the deep neural network; and using the synthesised plurality of one or more elements to provide a representation of the feature identified by the deep neural network in the plurality of samples associated with the outcome. Accordingly, rather than visualising a single instance of one or more elements in a sample which trigger a feature associated with an outcome, it is possible to visualise a range of samples including elements which would trigger a feature associated with an outcome, thus enabling a more comprehensive view of operation of a deep neural network in relation to a particular feature.