Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation
  for Pixel-wise Regression
Anton Baumann Thomas Roßberg Michael Schmitt
Uncertainty estimation in machine learning is paramount for enhancing the
reliability and interpretability of predictive models, especially in
high-stakes real-world scenarios. Despite the availability of numerous methods,
they often pose a trade-off between the quality of uncertainty estimation and
computational efficiency. Addressing this challenge, we present an adaptation
of the Multiple-Input Multiple-Output (MIMO) framework -- an approach
exploiting the overparameterization of deep neural networks -- for pixel-wise
regression tasks. Our MIMO variant expands the applicability of the approach
from simple image classification to broader computer vision domains. For that
purpose, we adapted the U-Net architecture to train multiple subnetworks within
a single model, harnessing the overparameterization in deep neural networks.
Additionally, we introduce a novel procedure for synchronizing subnetwork
performance within the MIMO framework. Our comprehensive evaluations of the
resulting MIMO U-Net on two orthogonal datasets demonstrate comparable accuracy
to existing models, superior calibration on in-distribution data, robust
out-of-distribution detection capabilities, and considerable improvements in
parameter size and inference time. Code available at
github.com/antonbaumann/MIMO-Unet