论文标题
通过不确定性的分配蒸馏，计算机视觉中的实时不确定性估计
Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distillation
论文作者
论文摘要
对不确定性的校准估计对于许多真实世界的计算机视觉应用至关重要。尽管有几种广泛使用的不确定性估计方法，但由于其简单性和有效性而脱颖而出。但是，该技术需要在推理期间多次通过网络通过网络，因此资源密集型无法在实时应用程序中部署。我们提出了一种简单，易于优化的蒸馏方法，用于学习预先训练的辍学模型的条件预测分布，以在计算机视觉任务中快速，无样品不确定性估计。我们从经验上测试了所提出的方法在语义分割和深度估计任务上的有效性，并证明我们的方法可以大大减少推理时间，实现实时不确定性量化，同时实现不确定性估计和预测性能在常规辍学模型上的提高质量。
Calibrated estimates of uncertainty are critical for many real-world computer vision applications of deep learning. While there are several widely-used uncertainty estimation methods, dropout inference stands out for its simplicity and efficacy. This technique, however, requires multiple forward passes through the network during inference and therefore can be too resource-intensive to be deployed in real-time applications. We propose a simple, easy-to-optimize distillation method for learning the conditional predictive distribution of a pre-trained dropout model for fast, sample-free uncertainty estimation in computer vision tasks. We empirically test the effectiveness of the proposed method on both semantic segmentation and depth estimation tasks and demonstrate our method can significantly reduce the inference time, enabling real-time uncertainty quantification, while achieving improved quality of both the uncertainty estimates and predictive performance over the regular dropout model.
