DUDES: Deep Uncertainty Distillation using Ensembles for Semantic Segmentation

TL;DR

This work presents a novel approach for efficient and reliable uncertainty estimation for semantic segmentation, which is called DUDES, which applies student-teacher distillation with a Deep Ensemble to accurately approximate predictive uncertainties with a single forward pass while maintaining simplicity and adaptability.

Abstract

Deep neural networks lack interpretability and tend to be overconfident, which poses a serious problem in safety-critical applications like autonomous driving, medical imaging, or machine vision tasks with high demands on reliability. Quantifying the predictive uncertainty is a promising endeavour to open up the use of deep neural networks for such applications. Unfortunately, current available methods are computationally expensive. In this work, we present a novel approach for efficient and reliable uncertainty estimation which we call Deep Uncertainty Distillation using Ensembles for Segmentation (DUDES). DUDES applies student-teacher distillation with a Deep Ensemble to accurately approximate predictive uncertainties with a single forward pass while maintaining simplicity and adaptability. Experimentally, DUDES accurately captures predictive uncertainties without sacrificing performance on the segmentation task and indicates impressive capabilities of identifying wrongly classified pixels and out-of-domain samples on the Cityscapes dataset. With DUDES, we manage to simultaneously simplify and outperform previous work on Deep Ensemble-based Uncertainty Distillation.

Figures (5)

• Figure 1: dudes applies student-teacher distillation with a Deep Ensemble (DE) to accurately approximate predictive uncertainties with a single forward pass while maintaining simplicity and adaptability.
• Figure 2: A schematic overview of the training process of dudes. dudes is an easy-to-adapt framework for efficiently estimating predictive uncertainty through student-teacher distillation. The student model simultaneously outputs a segmentation prediction alongside a corresponding uncertainty prediction. Training the student involves a regular segmentation loss with the ground truth labels and an additional uncertainty loss. As ground truth uncertainties, we compute the predictive uncertainty of a de, thereby acting as the teacher.
• Figure 3: Example images from the Cityscapes validation set (a) with corresponding ground truth labels (b), our student's segmentation predictions (c), a binary accuracy map (d), and the student's uncertainty prediction (e). White pixels in the binary accuracy map are either incorrect predictions or void classes, which appear black in the ground truth label. For the uncertainty prediction, brighter pixels represent higher predictive uncertainties.
• Figure 4: Comparison between the student's and the teacher's mean Intersection over Union (mIoU). We progressively ignore an increasing percentage of pixels in the segmentation prediction and simultaneously re-evaluated the mIoU. The pixels are sorted based on their predictive uncertainty in descending order, thus removing the most uncertain segmentation predictions first.
• Figure 5: Ablation study on the impact of the number of ensemble members on the mean Intersection over Union (mIoU) and mean Uncertainty (mUnc).