Unmodified mixtures of experts can provide predictive uncertainty estimates via entropy, mutual information, and expert-variance, and these estimates outperform a two-expert average ensemble on conditional correctness metrics under out-of-distribution data.
Encoder-decoder with atrous separable convolution for semantic image segmentation
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Extracting Uncertainty Estimates from Mixtures of Experts for Semantic Segmentation
Unmodified mixtures of experts can provide predictive uncertainty estimates via entropy, mutual information, and expert-variance, and these estimates outperform a two-expert average ensemble on conditional correctness metrics under out-of-distribution data.