Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation

Abstract

Recent studies have shown that ensemble approaches could not only improve accuracy and but also estimate model uncertainty in deep learning. However, it requires a large number of parameters according to the increase of ensemble models for better prediction and uncertainty estimation. To address this issue, a generic and efficient segmentation framework to construct ensemble segmentation models is devised in this paper. In the proposed method, ensemble models can be efficiently generated by using the stochastic layer selection method. The ensemble models are trained to estimate uncertainty through Bayesian approximation. Moreover, to overcome its limitation from uncertain instances, we devise a new pixel-wise uncertainty loss, which improves the predictive performance. To evaluate our method, comprehensive and comparative experiments have been conducted on two datasets. Experimental results show that the proposed method could provide useful uncertainty information by Bayesian approximation with the efficient ensemble model generation and improve the predictive performance.

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

“…The results of our research show that although a variety of ensemble methods [474], [475], [476], [477], [478], [479], [480] also have a high ability to deal with uncertainty along with good performance and optimizing the performance of other methods, these high capabilities of these methods have not been used more significantly. In other words, we noticed that these methods have performed remarkably well in few studies.…”