Neural Network Adversarial Calibration Training
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Solution Overview
Problem
Current AI systems lack robustness and trustworthiness, particularly in real-world applications like autonomous driving and industrial IoT settings, where predictive models face uncertain and out-of-domain inputs, leading to uncalibrated and unreliable predictions due to high or low predictive entropy.
Innovation Solution
A new training strategy combining conventional loss with an entropy-encouraging loss term and an adversarial calibration loss term, enabling well-calibrated and trustworthy predictions across various data modalities and model architectures, including LSTMs or GRUs, by iteratively updating neural network weights based on categorical cross-entropy, predictive entropy, and adversarial calibration losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional loss functions are used for training neural networks, then in-domain prediction accuracy is improved, but predictive uncertainty calibration deteriorates for out-of-domain samples
Solution Approach 1:
The patent combines conventional cross-entropy loss with predictive entropy regularization and adversarial calibration loss functions into a unified training objective. This merging allows the model to simultaneously optimize for in-domain prediction accuracy while maintaining proper uncertainty calibration for out-of-domain samples through the integrated multi-component loss function.
Solution Approach 2:
The patent introduces hyperparameters (λ and α) to control the weighting of different loss components during training. By adjusting these parameters, the training process can balance between maximizing prediction accuracy and maintaining uncertainty calibration, allowing flexible optimization based on specific application requirements.
2Reliability
If predictive entropy is increased to improve uncertainty awareness, then out-of-domain detection capability is improved, but prediction confidence becomes overly conservative
Solution Approach 1:
The patent applies different loss function components to different aspects of prediction behavior: cross-entropy loss optimizes for accurate predictions on in-domain samples, while predictive entropy regularization specifically targets uncertainty calibration for out-of-domain samples. This local optimization approach allows high prediction confidence for well-known inputs while maintaining appropriate uncertainty awareness for unfamiliar inputs.
Solution Approach 2:
The training process dynamically balances between maximizing prediction confidence and maintaining uncertainty awareness through the combined loss function. The model learns to adapt its confidence levels based on the input characteristics, being more confident for in-domain samples and more uncertain for out-of-domain samples, rather than applying a fixed confidence level.
3Object-affected harmful factors
If adversarial training is applied to improve robustness, then resistance to adversarial attacks is improved, but training complexity and computational cost increase
Solution Approach 1:
The patent incorporates adversarial calibration loss during the training phase to preemptively improve robustness against adversarial attacks. By including this regularization term in the training objective, the model learns to maintain proper uncertainty calibration even when exposed to adversarial perturbations, reducing the need for separate post-training robustness procedures.
Solution Approach 2:
The adversarial calibration loss serves multiple functions simultaneously: it improves robustness to adversarial attacks, maintains uncertainty calibration for out-of-domain samples, and works alongside conventional loss functions for in-domain accuracy. This multi-functionality reduces the need for separate specialized training procedures.
Data Source
AI summary
The disclosed relates to a computer-implemented method of training a Neural Network as well as a corresponding computer program, computer-readable medium and data processing system. In addition to a categorical cross-entropy loss LCCE weights of the NN are updated based on predictive entropy loss LS and an adversarial calibration loss Ladv.