Single-Model Epistemic Uncertainty Estimation With Adaptive Noise
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Solution Overview
Problem
Existing AI models struggle to robustly estimate epistemic uncertainty using a single model, as variance in outputs is not always equivalent to uncertainty, and methods like multi-output techniques require multiple inferences, which are inefficient for real-time applications.
Innovation Solution
A single model-based learning method that generates output distributions by using a feature network and a transformed network with adaptive noise, calculating ground truth and similarity losses to update weights through backpropagation, allowing the model to estimate epistemic uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single model generates output distributions to estimate epistemic uncertainty, then the model can express uncertainty without requiring multiple models, but the variance of outputs is not always equivalent to uncertainty and small weight changes cause significant variance changes
Solution Approach 1:
The patent creates a transformed network by copying the base network's architecture and weights, then introduces adaptive noise to the copied weights. This copying mechanism allows the system to generate multiple output distributions from a single model structure, enabling uncertainty estimation without requiring multiple distinct models. The transformed network serves as a noisy variant of the base network, producing varied outputs that reflect epistemic uncertainty while maintaining the simplicity of a single-model architecture.
Solution Approach 2:
The patent applies adaptive noise to the network weights as a parameter transformation. By adding noise with varying magnitude to the copied network weights, the system generates different output distributions from the same base network. This parameter change approach allows the single model to produce multiple plausible outputs, and the variance among these outputs serves as a measure of epistemic uncertainty. The adaptive noise magnitude adjusts based on the network's learning state, providing stable uncertainty estimation.
2Device complexity
If multi-output techniques using dropout and multiple inferences are used to estimate uncertainty, then epistemic uncertainty can be estimated using a single model, but multiple repetitions are required during inference which reduces real-time performance
Solution Approach 1:
The patent performs the noise addition transformation during the training phase rather than during inference. By pre-computing the transformed network with adaptive noise incorporated into the weights during training, the system eliminates the need for multiple repeated inferences during the actual prediction phase. The trained transformed network can directly produce output distributions in a single forward pass, maintaining real-time performance while enabling uncertainty estimation through the variance of outputs from the noisy transformed network.
Data Source
AI summary
A single model-based learning method for estimating the uncertainty of an artificial intelligence (AI) model, the method comprising: generating an output distribution from a base network and a transformed output distribution from a transformed network, based on a result value generated by a feature network, wherein the transformed network is generated by applying adaptive noise to the base network; calculating a ground truth loss based on a difference between a ground truth distribution and the output distribution, and a similarity loss based on a difference between the output distribution and the transformed output distribution; and training the AI model, which includes the feature network and the base network, by updating the weights of the feature network and the base network through backpropagation of the ground truth loss and the similarity loss.