Interpretable Deep Learning via Disentangled Latent Features
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Solution Overview
Problem
Current deep learning models lack interpretability, making it challenging to understand how they arrive at specific decisions, which hinders refinement and improvement of their training processes.
Innovation Solution
The proposed method involves training an interpretable deep learning model by using a deep neural network to extract features, generating a latent space of vectors, and feeding these vectors to a task-specific model to produce interpretable predictions of feature dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a complex deep architecture is used, then the model can learn difficult decision boundaries and perform really well, but the model becomes uninterpretable
Solution Approach 1:
The patent segments the deep learning model into multiple layers, with each layer learning specific features. The final layer produces predictions while intermediate layers extract interpretable features. This segmentation allows the model to maintain complex architecture for high performance while making individual components interpretable through their specific feature extraction functions.
2Device complexity
If a simple model architecture is used, then the model is interpretable, but it cannot learn complicated decision boundaries and performance suffers
Solution Approach 1:
The patent introduces a new dimension of interpretability by extracting and analyzing intermediate features from each layer. Instead of treating the model as a black box, it adds a dimension of visibility into the decision-making process by examining what features each layer learns, thereby maintaining simple interpretability while using complex architectures for high performance.
3Device complexity
If an explanator module is added to provide interpretability, then explanation is generated, but the explanation is based on guesses and not necessarily a true explanation of how the learning model arrived at its output
Solution Approach 1:
The patent enables the deep learning model to explain its own decisions through the features it naturally learns during training. Each layer's intermediate features serve as self-generated explanations of what the model is looking at and how it processes information. This self-service approach eliminates the need for external explanator modules that make guesses, providing truly accurate explanations based on the model's actual internal representations.
Data Source
AI summary
A method and system of training an interpretable deep learning model includes receiving an input set of data, which may be complex. The input set of data is provided to deep learning model for feature extraction. In an exemplary embodiment, the deep learning model generates a disentangled latent space of features from the feature extraction. The features may comprise semantically meaningful data which is then provided to a low-complexity learning model. The low-complexity learning model generates output based on a specified task (for example, classification or regression). Being a low-complexity learning model provides confidence that the data output from the deep learning model is inherently interpretable.