Evidential Deep Learning Uncertainty Analysis
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Solution Overview
Problem
Conventional deep learning models often make unreliable predictions, particularly in safety-critical tasks like autonomous driving, due to the lack of reliable uncertainty estimation, and existing approaches fail to capture informative higher-order structures and predict out-of-distribution data effectively.
Innovation Solution
The use of Evidential Deep Learning (EDL) with dissonance regularization and recurrent priors, which employs a Dirichlet distribution to generate representations of epistemic and aleatoric uncertainty, and enforces minimization of conflicting beliefs during model training to improve predictive performance and uncertainty estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional deep learning models are used for prediction, then the model structure is simple and training is fast, but the predictions are unreliable and lack uncertainty estimation
Solution Approach 1:
The patent changes the parameter representation by using Dirichlet distribution parameters instead of standard neural network outputs. This allows the model to output uncertainty estimates directly while maintaining the core prediction functionality, resolving the contradiction between reliability and complexity.
Solution Approach 2:
The patent introduces evidential deep learning as an intermediary layer between the neural network and the final prediction. This intermediary computes uncertainty estimates and calibrated predictions, improving reliability without requiring complete model redesign.
2Reliability
If existing uncertainty estimation approaches are used, then some uncertainty information is provided, but higher-order structures and out-of-distribution data prediction fail
Solution Approach 1:
The patent moves from scalar or simple distribution uncertainty estimates to Dirichlet distribution parameters, adding dimensional information about uncertainty. This higher-order representation captures more nuanced uncertainty structures and improves out-of-distribution detection capability.
Solution Approach 2:
The patent performs preliminary calibration of predictions during training by minimizing the discrepancy between predicted Dirichlet parameters and empirical distributions. This preliminary action ensures better uncertainty estimation and out-of-distribution handling during deployment.
3Reliability
If dissonance regularization is applied to minimize conflicting beliefs, then predictive performance improves, but the training process becomes more complex
Solution Approach 1:
The patent implements dissonance regularization as a feedback mechanism during training that penalizes conflicting beliefs in the Dirichlet distribution. This feedback loop guides the model to produce more consistent predictions, improving performance while keeping the training process manageable through a well-defined loss function.
Data Source
AI summary
Disclosed is an example solution to analyze uncertainty of an evidential deep learning neural network with dissonance regularization and recurrent priors. An example apparatus includes processor circuitry to at least one of instantiate or execute the machine readable instructions to receive a first predicted classification of a first input of an evidential deep learning neural network (EVDL NN), identify a first uncertainty metric associated with the EVDL NN, the first uncertainty metric corresponding to the first input of the EVDL NN, calculate a first dissonance score based on the first uncertainty metric, and when the first dissonance score satisfies a threshold, assign the first predicted classification to the first input.