Cycle Consistency-based Uncertainty Quantification of Neural Networks in
  Inverse Imaging Problems
Luzhe Huang Jianing Li Xiaofu Ding Yijie Zhang Hanlong Chen Aydogan Ozcan
Uncertainty estimation is critical for numerous applications of deep neural
networks and draws growing attention from researchers. Here, we demonstrate an
uncertainty quantification approach for deep neural networks used in inverse
problems based on cycle consistency. We build forward-backward cycles using the
physical forward model available and a trained deep neural network solving the
inverse problem at hand, and accordingly derive uncertainty estimators through
regression analysis on the consistency of these forward-backward cycles. We
theoretically analyze cycle consistency metrics and derive their relationship
with respect to uncertainty, bias, and robustness of the neural network
inference. To demonstrate the effectiveness of these cycle consistency-based
uncertainty estimators, we classified corrupted and out-of-distribution input
image data using some of the widely used image deblurring and super-resolution
neural networks as testbeds. The blind testing of our method outperformed other
models in identifying unseen input data corruption and distribution shifts.
This work provides a simple-to-implement and rapid uncertainty quantification
method that can be universally applied to various neural networks used for
solving inverse problems.