Information Robust Dirichlet Networks for Predictive Uncertainty Estimation
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Precise estimation of uncertainty in predictions for AI systems is a critical factor in ensuring trust and safety. Conventional neural networks tend to be overconfident as they do not account for uncertainty during training. In contrast to Bayesian neural networks that learn approximate distributions on weights to infer prediction confidence, we propose a novel method, Information Robust Dirichlet networks, that learns the Dirichlet distribution on prediction probabilities by minimizing the expected L_p norm of the prediction error and an information divergence loss that penalizes information flow towards incorrect classes, while simultaneously maximizing differential entropy of small adversarial perturbations to provide accurate uncertainty estimates. Properties of the new cost function are derived to indicate how improved uncertainty estimation is achieved. Experiments using real datasets show that our technique outperforms state-of-the-art neural networks, by a large margin, for estimating in-distribution and out-of-distribution uncertainty, and detecting adversarial examples.
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